Computer processing of a language with rules that reflect and describe current usage rather than prescribed usage.
A research and development program initiated by the NATIONAL LIBRARY OF MEDICINE to build knowledge sources for the purpose of aiding the development of systems that help health professionals retrieve and integrate biomedical information. The knowledge sources can be used to link disparate information systems to overcome retrieval problems caused by differences in terminology and the scattering of relevant information across many databases. The three knowledge sources are the Metathesaurus, the Semantic Network, and the Specialist Lexicon.
Organized activities related to the storage, location, search, and retrieval of information.
The gradual expansion in complexity and meaning of symbols and sounds as perceived and interpreted by the individual through a maturational and learning process. Stages in development include babbling, cooing, word imitation with cognition, and use of short sentences.
The science of language, including phonetics, phonology, morphology, syntax, semantics, pragmatics, and historical linguistics. (Random House Unabridged Dictionary, 2d ed)
The relationships between symbols and their meanings.
A specified list of terms with a fixed and unalterable meaning, and from which a selection is made when CATALOGING; ABSTRACTING AND INDEXING; or searching BOOKS; JOURNALS AS TOPIC; and other documents. The control is intended to avoid the scattering of related subjects under different headings (SUBJECT HEADINGS). The list may be altered or extended only by the publisher or issuing agency. (From Harrod's Librarians' Glossary, 7th ed, p163)
Conditions characterized by deficiencies of comprehension or expression of written and spoken forms of language. These include acquired and developmental disorders.
Use of sophisticated analysis tools to sort through, organize, examine, and combine large sets of information.
Specific languages used to prepare computer programs.
Activities performed to identify concepts and aspects of published information and research reports.
Computer-based systems for input, storage, display, retrieval, and printing of information contained in a patient's medical record.
Conditions characterized by language abilities (comprehension and expression of speech and writing) that are below the expected level for a given age, generally in the absence of an intellectual impairment. These conditions may be associated with DEAFNESS; BRAIN DISEASES; MENTAL DISORDERS; or environmental factors.
Terms or expressions which provide the major means of access by subject to the bibliographic unit.
A system of hand gestures used for communication by the deaf or by people speaking different languages.
Media that facilitate transportability of pertinent information concerning patient's illness across varied providers and geographic locations. Some versions include direct linkages to online consumer health information that is relevant to the health conditions and treatments related to a specific patient.
The terms, expressions, designations, or symbols used in a particular science, discipline, or specialized subject area.
The premier bibliographic database of the NATIONAL LIBRARY OF MEDICINE. MEDLINE® (MEDLARS Online) is the primary subset of PUBMED and can be searched on NLM's Web site in PubMed or the NLM Gateway. MEDLINE references are indexed with MEDICAL SUBJECT HEADINGS (MeSH).
Theory and development of COMPUTER SYSTEMS which perform tasks that normally require human intelligence. Such tasks may include speech recognition, LEARNING; VISUAL PERCEPTION; MATHEMATICAL COMPUTING; reasoning, PROBLEM SOLVING, DECISION-MAKING, and translation of language.
Shortened forms of written words or phrases used for brevity.
A system of record keeping in which a list of the patient's problems is made and all history, physical findings, laboratory data, etc. pertinent to each problem are placed under that heading.
A standardized nomenclature for clinical drugs and drug delivery devices. It links its names to many of the drug vocabularies commonly used in pharmacy management.
Lists of words, usually in alphabetical order, giving information about form, pronunciation, etymology, grammar, and meaning.
Social media model for enabling public involvement and recruitment in participation. Use of social media to collect feedback and recruit volunteer subjects.
The portion of an interactive computer program that issues messages to and receives commands from a user.
Rehabilitation of persons with language disorders or training of children with language development disorders.
Controlled vocabulary of clinical terms produced by the International Health Terminology Standards Development Organisation (IHTSDO).
A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.
Software designed to store, manipulate, manage, and control data for specific uses.
A bibliographic database that includes MEDLINE as its primary subset. It is produced by the National Center for Biotechnology Information (NCBI), part of the NATIONAL LIBRARY OF MEDICINE. PubMed, which is searchable through NLM's Web site, also includes access to additional citations to selected life sciences journals not in MEDLINE, and links to other resources such as the full-text of articles at participating publishers' Web sites, NCBI's molecular biology databases, and PubMed Central.
A management function in which standards and guidelines are developed for the development, maintenance, and handling of forms and records.
Computer programs based on knowledge developed from consultation with experts on a problem, and the processing and/or formalizing of this knowledge using these programs in such a manner that the problems may be solved.
Collections of facts, assumptions, beliefs, and heuristics that are used in combination with databases to achieve desired results, such as a diagnosis, an interpretation, or a solution to a problem (From McGraw Hill Dictionary of Scientific and Technical Terms, 6th ed).
A discipline concerned with relations between messages and the characteristics of individuals who select and interpret them; it deals directly with the processes of encoding (phonetics) and decoding (psychoacoustics) as they relate states of messages to states of communicators.
A verbal or nonverbal means of communicating ideas or feelings.
Structured vocabularies describing concepts from the fields of biology and relationships between concepts.
In INFORMATION RETRIEVAL, machine-sensing or identification of visible patterns (shapes, forms, and configurations). (Harrod's Librarians' Glossary, 7th ed)
Sequential operating programs and data which instruct the functioning of a digital computer.
A specialty concerned with the nature and cause of disease as expressed by changes in cellular or tissue structure and function caused by the disease process.
The application of a concept to that which it is not literally the same but which suggests a resemblance and comparison. Medical metaphors were widespread in ancient literature; the description of a sick body was often used by ancient writers to define a critical condition of the State, in which one corrupt part can ruin the entire system. (From Med Secoli Arte Sci, 1990;2(3):abstract 331)
Recording of pertinent information concerning patient's illness or illnesses.
Information systems, usually computer-assisted, designed to store, manipulate, and retrieve information for planning, organizing, directing, and controlling administrative activities associated with the provision and utilization of radiology services and facilities.
Systematic organization, storage, retrieval, and dissemination of specialized information, especially of a scientific or technical nature (From ALA Glossary of Library and Information Science, 1983). It often involves authenticating or validating information.
Skills in the use of language which lead to proficiency in written or spoken communication.
Data processing largely performed by automatic means.
The sum or the stock of words used by a language, a group, or an individual. (From Webster, 3d ed)
The field of information science concerned with the analysis and dissemination of medical data through the application of computers to various aspects of health care and medicine.
The act or practice of literary composition, the occupation of writer, or producing or engaging in literary work as a profession.
The act, process, or an instance of narrating, i.e., telling a story. In the context of MEDICINE or ETHICS, narration includes relating the particular and the personal in the life story of an individual.
Software capable of recognizing dictation and transcribing the spoken words into written text.
An agency of the NATIONAL INSTITUTES OF HEALTH concerned with overall planning, promoting, and administering programs pertaining to advancement of medical and related sciences. Major activities of this institute include the collection, dissemination, and exchange of information important to the progress of medicine and health, research in medical informatics and support for medical library development.
A medical dictionary is a specialized reference book containing terms, definitions, and explanations related to medical science, healthcare practices, and associated disciplines, used by healthcare professionals, students, researchers, and patients to enhance understanding of medical concepts and terminology.
Extensive collections, reputedly complete, of facts and data garnered from material of a specialized subject area and made available for analysis and application. The collection can be automated by various contemporary methods for retrieval. The concept should be differentiated from DATABASES, BIBLIOGRAPHIC which is restricted to collections of bibliographic references.
Extensive collections, reputedly complete, of references and citations to books, articles, publications, etc., generally on a single subject or specialized subject area. Databases can operate through automated files, libraries, or computer disks. The concept should be differentiated from DATABASES, FACTUAL which is used for collections of data and facts apart from bibliographic references to them.
Controlled vocabulary thesaurus produced by the NATIONAL LIBRARY OF MEDICINE. It consists of sets of terms naming descriptors in a hierarchical structure that permits searching at various levels of specificity.
Computerized compilations of information units (text, sound, graphics, and/or video) interconnected by logical nonlinear linkages that enable users to follow optimal paths through the material and also the systems used to create and display this information. (From Thesaurus of ERIC Descriptors, 1994)
A publication issued at stated, more or less regular, intervals.
Detailed account or statement or formal record of data resulting from empirical inquiry.
X-ray visualization of the chest and organs of the thoracic cavity. It is not restricted to visualization of the lungs.
The administrative process of discharging the patient, alive or dead, from hospitals or other health facilities.
A field of biology concerned with the development of techniques for the collection and manipulation of biological data, and the use of such data to make biological discoveries or predictions. This field encompasses all computational methods and theories for solving biological problems including manipulation of models and datasets.
Involuntary ("parrot-like"), meaningless repetition of a recently heard word, phrase, or song. This condition may be associated with transcortical APHASIA; SCHIZOPHRENIA; or other disorders. (From Adams et al., Principles of Neurology, 6th ed, p485)
Bone marrow-derived lymphocytes that possess cytotoxic properties, classically directed against transformed and virus-infected cells. Unlike T CELLS; and B CELLS; NK CELLS are not antigen specific. The cytotoxicity of natural killer cells is determined by the collective signaling of an array of inhibitory and stimulatory CELL SURFACE RECEPTORS. A subset of T-LYMPHOCYTES referred to as NATURAL KILLER T CELLS shares some of the properties of this cell type.
Overall systems, traditional or automated, to provide medication to patients.
A branch of biology dealing with the structure of organisms.
Includes both producing and responding to words, either written or spoken.
Application of computer programs designed to assist the physician in solving a diagnostic problem.
The determination of the nature of a disease or condition, or the distinguishing of one disease or condition from another. Assessment may be made through physical examination, laboratory tests, or the likes. Computerized programs may be used to enhance the decision-making process.
A loose confederation of computer communication networks around the world. The networks that make up the Internet are connected through several backbone networks. The Internet grew out of the US Government ARPAnet project and was designed to facilitate information exchange.
Theoretical representations that simulate the behavior or activity of systems, processes, or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.
Integrated, computer-assisted systems designed to store, manipulate, and retrieve information concerned with the administrative and clinical aspects of providing medical services within the hospital.
Systems composed of a computer or computers, peripheral equipment, such as disks, printers, and terminals, and telecommunications capabilities.
The systematic arrangement of entities in any field into categories classes based on common characteristics such as properties, morphology, subject matter, etc.
Specifications and instructions applied to the software.
Computer-based information systems used to integrate clinical and patient information and provide support for decision-making in patient care.
The statistical reproducibility of measurements (often in a clinical context), including the testing of instrumentation or techniques to obtain reproducible results. The concept includes reproducibility of physiological measurements, which may be used to develop rules to assess probability or prognosis, or response to a stimulus; reproducibility of occurrence of a condition; and reproducibility of experimental results.
The act or fact of grasping the meaning, nature, or importance of; understanding. (American Heritage Dictionary, 4th ed) Includes understanding by a patient or research subject of information disclosed orally or in writing.
The procedures involved in combining separately developed modules, components, or subsystems so that they work together as a complete system. (From McGraw-Hill Dictionary of Scientific and Technical Terms, 4th ed)
A system of categories to which morbid entries are assigned according to established criteria. Included is the entire range of conditions in a manageable number of categories, grouped to facilitate mortality reporting. It is produced by the World Health Organization (From ICD-10, p1). The Clinical Modifications, produced by the UNITED STATES DEPT. OF HEALTH AND HUMAN SERVICES, are larger extensions used for morbidity and general epidemiological purposes, primarily in the U.S.
The artificial language of schizophrenic patients - neologisms (words of the patient's own making with new meanings).
Communication through a system of conventional vocal symbols.
Those factors, such as language or sociocultural relationships, which interfere in the meaningful interpretation and transmission of ideas between individuals or groups.
The science or study of speech sounds and their production, transmission, and reception, and their analysis, classification, and transcription. (Random House Unabridged Dictionary, 2d ed)
Acquiring information from a patient on past medical conditions and treatments.
Research that involves the application of the natural sciences, especially biology and physiology, to medicine.
A general term for the complete loss of the ability to hear from both ears.
'Reading' in a medical context often refers to the act or process of a person interpreting and comprehending written or printed symbols, such as letters or words, for the purpose of deriving information or meaning from them.
A computer in a medical context is an electronic device that processes, stores, and retrieves data, often used in medical settings for tasks such as maintaining patient records, managing diagnostic images, and supporting clinical decision-making through software applications and tools.
The continuous developmental process of a culture from simple to complex forms and from homogeneous to heterogeneous qualities.
A definite pathologic process with a characteristic set of signs and symptoms. It may affect the whole body or any of its parts, and its etiology, pathology, and prognosis may be known or unknown.
Databases devoted to knowledge about specific genes and gene products.
Acquired or developmental conditions marked by an impaired ability to comprehend or generate spoken forms of language.
Measurement of parameters of the speech product such as vocal tone, loudness, pitch, voice quality, articulation, resonance, phonation, phonetic structure and prosody.
The term "United States" in a medical context often refers to the country where a patient or study participant resides, and is not a medical term per se, but relevant for epidemiological studies, healthcare policies, and understanding differences in disease prevalence, treatment patterns, and health outcomes across various geographic locations.
A graphic device used in decision analysis, series of decision options are represented as branches (hierarchical).
The circulation or wide dispersal of information.
Persons with any degree of loss of hearing that has an impact on their activities of daily living or that requires special assistance or intervention.
Methods for determining interaction between PROTEINS.
Behavioral manifestations of cerebral dominance in which there is preferential use and superior functioning of either the left or the right side, as in the preferred use of the right hand or right foot.
Systems developed for collecting reports from government agencies, manufacturers, hospitals, physicians, and other sources on adverse drug reactions.
Conversion from one language to another language.
Binary classification measures to assess test results. Sensitivity or recall rate is the proportion of true positives. Specificity is the probability of correctly determining the absence of a condition. (From Last, Dictionary of Epidemiology, 2d ed)
Integrated set of files, procedures, and equipment for the storage, manipulation, and retrieval of information.
Organized collections of computer records, standardized in format and content, that are stored in any of a variety of computer-readable modes. They are the basic sets of data from which computer-readable files are created. (from ALA Glossary of Library and Information Science, 1983)
Computer systems or networks designed to provide radiographic interpretive information.
Studies determining the effectiveness or value of processes, personnel, and equipment, or the material on conducting such studies. For drugs and devices, CLINICAL TRIALS AS TOPIC; DRUG EVALUATION; and DRUG EVALUATION, PRECLINICAL are available.
Treatment for individuals with speech defects and disorders that involves counseling and use of various exercises and aids to help the development of new speech habits.
Imaging techniques used to colocalize sites of brain functions or physiological activity with brain structures.
The process whereby an utterance is decoded into a representation in terms of linguistic units (sequences of phonetic segments which combine to form lexical and grammatical morphemes).
Learning to respond verbally to a verbal stimulus cue.
Drugs intended for human or veterinary use, presented in their finished dosage form. Included here are materials used in the preparation and/or formulation of the finished dosage form.
Controlled operation of an apparatus, process, or system by mechanical or electronic devices that take the place of human organs of observation, effort, and decision. (From Webster's Collegiate Dictionary, 1993)
Non-invasive method of demonstrating internal anatomy based on the principle that atomic nuclei in a strong magnetic field absorb pulses of radiofrequency energy and emit them as radiowaves which can be reconstructed into computerized images. The concept includes proton spin tomographic techniques.
Dominance of one cerebral hemisphere over the other in cerebral functions.

A reliability study for evaluating information extraction from radiology reports. (1/1342)

GOAL: To assess the reliability of a reference standard for an information extraction task. SETTING: Twenty-four physician raters from two sites and two specialties judged whether clinical conditions were present based on reading chest radiograph reports. METHODS: Variance components, generalizability (reliability) coefficients, and the number of expert raters needed to generate a reliable reference standard were estimated. RESULTS: Per-rater reliability averaged across conditions was 0.80 (95% CI, 0.79-0.81). Reliability for the nine individual conditions varied from 0.67 to 0.97, with central line presence and pneumothorax the most reliable, and pleural effusion (excluding CHF) and pneumonia the least reliable. One to two raters were needed to achieve a reliability of 0.70, and six raters, on average, were required to achieve a reliability of 0.95. This was far more reliable than a previously published per-rater reliability of 0.19 for a more complex task. Differences between sites were attributable to changes to the condition definitions. CONCLUSION: In these evaluations, physician raters were able to judge very reliably the presence of clinical conditions based on text reports. Once the reliability of a specific rater is confirmed, it would be possible for that rater to create a reference standard reliable enough to assess aggregate measures on a system. Six raters would be needed to create a reference standard sufficient to assess a system on a case-by-case basis. These results should help evaluators design future information extraction studies for natural language processors and other knowledge-based systems.  (+info)

Mandarin and English single word processing studied with functional magnetic resonance imaging. (2/1342)

The cortical organization of language in bilinguals remains disputed. We studied 24 right-handed fluent bilinguals: 15 exposed to both Mandarin and English before the age of 6 years; and nine exposed to Mandarin in early childhood but English only after the age of 12 years. Blood oxygen level-dependent contrast functional magnetic resonance imaging was performed while subjects performed cued word generation in each language. Fixation was the control task. In both languages, activations were present in the prefrontal, temporal, and parietal regions, and the supplementary motor area. Activations in the prefrontal region were compared by (1) locating peak activations and (2) counting the number of voxels that exceeded a statistical threshold. Although there were differences in the magnitude of activation between the pair of languages, no subject showed significant differences in peak-location or hemispheric asymmetry of activations in the prefrontal language areas. Early and late bilinguals showed a similar pattern of overlapping activations. There are no significant differences in the cortical areas activated for both Mandarin and English at the single word level, irrespective of age of acquisition of either language.  (+info)

A semantic lexicon for medical language processing. (3/1342)

OBJECTIVE: Construction of a resource that provides semantic information about words and phrases to facilitate the computer processing of medical narrative. DESIGN: Lexemes (words and word phrases) in the Specialist Lexicon were matched against strings in the 1997 Metathesaurus of the Unified Medical Language System (UMLS) developed by the National Library of Medicine. This yielded a "semantic lexicon," in which each lexeme is associated with one or more syntactic types, each of which can have one or more semantic types. The semantic lexicon was then used to assign semantic types to lexemes occurring in a corpus of discharge summaries (603,306 sentences). Lexical items with multiple semantic types were examined to determine whether some of the types could be eliminated, on the basis of usage in discharge summaries. A concordance program was used to find contrasting contexts for each lexeme that would reflect different semantic senses. Based on this evidence, semantic preference rules were developed to reduce the number of lexemes with multiple semantic types. RESULTS: Matching the Specialist Lexicon against the Metathesaurus produced a semantic lexicon with 75,711 lexical forms, 22,805 (30.1 percent) of which had two or more semantic types. Matching the Specialist Lexicon against one year's worth of discharge summaries identified 27,633 distinct lexical forms, 13,322 of which had at least one semantic type. This suggests that the Specialist Lexicon has about 79 percent coverage for syntactic information and 38 percent coverage for semantic information for discharge summaries. Of those lexemes in the corpus that had semantic types, 3,474 (12.6 percent) had two or more types. When semantic preference rules were applied to the semantic lexicon, the number of entries with multiple semantic types was reduced to 423 (1.5 percent). In the discharge summaries, occurrences of lexemes with multiple semantic types were reduced from 9.41 to 1.46 percent. CONCLUSION: Automatic methods can be used to construct a semantic lexicon from existing UMLS sources. This semantic information can aid natural language processing programs that analyze medical narrative, provided that lexemes with multiple semantic types are kept to a minimum. Semantic preference rules can be used to select semantic types that are appropriate to clinical reports. Further work is needed to increase the coverage of the semantic lexicon and to exploit contextual information when selecting semantic senses.  (+info)

Automatic identification of pneumonia related concepts on chest x-ray reports. (4/1342)

A medical language processing system called SymText, two other automated methods, and a lay person were compared against an internal medicine resident for their ability to identify pneumonia related concepts on chest x-ray reports. Sensitivity (recall), specificity, and positive predictive value (precision) are reported with respect to an independent panel of physicians. Overall the performance of SymText was similar to the physician and superior to the other methods. The automatic encoding of pneumonia concepts will support clinical research, decision making, computerized clinical protocols, and quality assurance in a radiology department.  (+info)

Mining molecular binding terminology from biomedical text. (5/1342)

Automatic access to information regarding macromolecular binding relationships would provide a valuable resource to the biomedical community. We report on a pilot project to mine such information from the molecular biology literature. The program being developed takes advantage of natural language processing techniques and is supported by two repositories of biomolecular knowledge. A formative evaluation has been conducted on a subset of MEDLINE abstracts.  (+info)

MEDTAG: tag-like semantics for medical document indexing. (6/1342)

Medical documentation is central in health care, as it constitutes the main means of communication between care providers. However, there is a gap to bridge between storing information and extracting the relevant underlying knowledge. We believe natural language processing (NLP) is the best solution to handle such a large amount of textual information. In this paper we describe the construction of a semantic tagset for medical document indexing purposes. Rather than attempting to produce a home-made tagset, we decided to use, as far as possible, standard medicine resources. This step has led us to choose UMLS hierarchical classes as a basis for our tagset. We also show that semantic tagging is not only providing bases for disambiguisation between senses, but is also useful in the query expansion process of the retrieval system. We finally focus on assessing the results of the semantic tagger.  (+info)

Use of the Extensible Stylesheet Language (XSL) for medical data transformation. (7/1342)

Recently, the Extensible Markup Language (XML) has received growing attention as a simple but flexible mechanism to represent medical data. As XML-based markups become more common there will be an increasing need to transform data stored in one XML markup into another markup. The Extensible Stylesheet Language (XSL) is a stylesheet language for XML. Development of a new mammography reporting system created a need to convert XML output from the MEDLee natural language processing system into a format suitable for cross-patient reporting. This paper examines the capability of XSL as a rule specification language that supports the medical XML data transformation. A set of nine relevant transformations was identified: Filtering, Substitution, Specification, Aggregation, Merging, Splitting, Transposition, Push-down and Pull-up. XSL-based methods for implementing these transformations are presented. The strengths and limitations of XSL are discussed in the context of XML medical data transformation.  (+info)

Analysis of biomedical text for chemical names: a comparison of three methods. (8/1342)

At the National Library of Medicine (NLM), a variety of biomedical vocabularies are found in data pertinent to its mission. In addition to standard medical terminology, there are specialized vocabularies including that of chemical nomenclature. Normal language tools including the lexically based ones used by the Unified Medical Language System (UMLS) to manipulate and normalize text do not work well on chemical nomenclature. In order to improve NLM's capabilities in chemical text processing, two approaches to the problem of recognizing chemical nomenclature were explored. The first approach was a lexical one and consisted of analyzing text for the presence of a fixed set of chemical segments. The approach was extended with general chemical patterns and also with terms from NLM's indexing vocabulary, MeSH, and the NLM SPECIALIST lexicon. The second approach applied Bayesian classification to n-grams of text via two different methods. The single lexical method and two statistical methods were tested against data from the 1999 UMLS Metathesaurus. One of the statistical methods had an overall classification accuracy of 97%.  (+info)

I'm sorry for any confusion, but "Natural Language Processing" (NLP) is actually a subfield of artificial intelligence that focuses on the interaction between computers and human language. It involves developing algorithms and software to understand, interpret, and generate human language in a valuable way.

In a medical context, NLP can be used to analyze electronic health records, clinical notes, and other forms of medical documentation to extract meaningful information, support clinical decision-making, and improve patient care. For example, NLP can help identify patients at risk for certain conditions, monitor treatment responses, and detect adverse drug events.

However, NLP is not a medical term or concept itself, so it doesn't have a specific medical definition.

The Unified Medical Language System (UMLS) is a set of files and software developed by the U.S. National Library of Medicine (NLM). It provides a comprehensive source of biomedical and health-related terms aimed at unifying and standardizing the language used in various areas of the medical field, such as clinical care, research, and education.

The UMLS includes many different vocabularies, classifications, and coding systems, including but not limited to:

* Systematized Nomenclature of Medicine--Clinical Terms (SNOMED CT)
* International Classification of Diseases (ICD)
* Current Procedural Terminology (CPT)
* Logical Observation Identifiers Names and Codes (LOINC)

By integrating these various terminologies, the UMLS enables more effective searching, information retrieval, and data analysis across different systems and databases. It also supports natural language processing (NLP) applications, such as text mining and clinical decision support systems.

'Information Storage and Retrieval' in the context of medical informatics refers to the processes and systems used for the recording, storing, organizing, protecting, and retrieving electronic health information (e.g., patient records, clinical data, medical images) for various purposes such as diagnosis, treatment planning, research, and education. This may involve the use of electronic health record (EHR) systems, databases, data warehouses, and other digital technologies that enable healthcare providers to access and share accurate, up-to-date, and relevant information about a patient's health status, medical history, and care plan. The goal is to improve the quality, safety, efficiency, and coordination of healthcare delivery by providing timely and evidence-based information to support clinical decision-making and patient engagement.

Language development refers to the process by which children acquire the ability to understand and communicate through spoken, written, or signed language. This complex process involves various components including phonology (sound system), semantics (meaning of words and sentences), syntax (sentence structure), and pragmatics (social use of language). Language development begins in infancy with cooing and babbling and continues through early childhood and beyond, with most children developing basic conversational skills by the age of 4-5 years. However, language development can continue into adolescence and even adulthood as individuals learn new languages or acquire more advanced linguistic skills. Factors that can influence language development include genetics, environment, cognition, and social interactions.

I'm sorry for any confusion, but "linguistics" is not a term that has a medical definition. Lingustics is the scientific study of language and its structure. It involves analysing language form, language meaning, and language in context.

If you have any questions related to healthcare or medicine, I'd be happy to try to help answer them!

I'm sorry for any confusion, but "semantics" is not a term that has a specific medical definition. Semantics is actually a branch of linguistics that deals with the study of meaning, reference, and the interpretation of signs and symbols, either individually or in combination. It is used in various fields including computer science, anthropology, psychology, and philosophy.

However, if you have any medical terms or concepts that you would like me to explain, I'd be happy to help!

A controlled vocabulary in a medical context refers to a specific set of standardized terms and phrases that are used in clinical documentation and communication. These vocabularies are often created and maintained by professional organizations or governmental bodies to ensure consistency, accuracy, and interoperability in the sharing and retrieval of health information.

Controlled vocabularies can include terminologies such as Systematized Nomenclature of Medicine (SNOMED), International Classification of Diseases (ICD), Logical Observation Identifiers Names and Codes (LOINC), and RxNorm, among others. By using a controlled vocabulary, healthcare providers can more easily share and analyze health data, support clinical decision-making, and facilitate accurate coding and billing.

Language disorders, also known as communication disorders, refer to a group of conditions that affect an individual's ability to understand or produce spoken, written, or other symbolic language. These disorders can be receptive (difficulty understanding language), expressive (difficulty producing language), or mixed (a combination of both).

Language disorders can manifest as difficulties with grammar, vocabulary, sentence structure, and coherence in communication. They can also affect social communication skills such as taking turns in conversation, understanding nonverbal cues, and interpreting tone of voice.

Language disorders can be developmental, meaning they are present from birth or early childhood, or acquired, meaning they develop later in life due to injury, illness, or trauma. Examples of acquired language disorders include aphasia, which can result from stroke or brain injury, and dysarthria, which can result from neurological conditions affecting speech muscles.

Language disorders can have significant impacts on an individual's academic, social, and vocational functioning, making it important to diagnose and treat them as early as possible. Treatment typically involves speech-language therapy to help individuals develop and improve their language skills.

Data mining, in the context of health informatics and medical research, refers to the process of discovering patterns, correlations, and insights within large sets of patient or clinical data. It involves the use of advanced analytical techniques such as machine learning algorithms, statistical models, and artificial intelligence to identify and extract useful information from complex datasets.

The goal of data mining in healthcare is to support evidence-based decision making, improve patient outcomes, and optimize resource utilization. Applications of data mining in healthcare include predicting disease outbreaks, identifying high-risk patients, personalizing treatment plans, improving clinical workflows, and detecting fraud and abuse in healthcare systems.

Data mining can be performed on various types of healthcare data, including electronic health records (EHRs), medical claims databases, genomic data, imaging data, and sensor data from wearable devices. However, it is important to ensure that data mining techniques are used ethically and responsibly, with appropriate safeguards in place to protect patient privacy and confidentiality.

I'm afraid there seems to be a misunderstanding. Programming languages are a field of study in computer science and are not related to medicine. They are used to create computer programs, through the composition of symbols and words. Some popular programming languages include Python, Java, C++, and JavaScript. If you have any questions about programming or computer science, I'd be happy to try and help answer them!

Abstracting and indexing are processes used in the field of information science to organize, summarize, and categorize published literature, making it easier for researchers and other interested individuals to find and access relevant information.

Abstracting involves creating a brief summary of a publication, typically no longer than a few hundred words, that captures its key points and findings. This summary is known as an abstract and provides readers with a quick overview of the publication's content, allowing them to determine whether it is worth reading in full.

Indexing, on the other hand, involves categorizing publications according to their subject matter, using a controlled vocabulary or set of keywords. This makes it easier for users to search for and find publications on specific topics, as they can simply look up the relevant keyword or subject heading in the index.

Together, abstracting and indexing are essential tools for managing the vast and growing amount of published literature in any given field. They help ensure that important research findings and other information are easily discoverable and accessible to those who need them, thereby facilitating the dissemination of knowledge and advancing scientific progress.

A Computerized Medical Record System (CMRS) is a digital version of a patient's paper chart. It contains all of the patient's medical history from multiple providers and can be shared securely between healthcare professionals. A CMRS includes a range of data such as demographics, progress notes, problems, medications, vital signs, past medical history, immunizations, laboratory data, and radiology reports. The system facilitates the storage, retrieval, and exchange of this information in an efficient manner, and can also provide decision support, alerts, reminders, and tools for performing data analysis and creating reports. It is designed to improve the quality, safety, and efficiency of healthcare delivery by providing accurate, up-to-date, and comprehensive information about patients at the point of care.

Language development disorders, also known as language impairments or communication disorders, refer to a group of conditions that affect an individual's ability to understand and/or use spoken or written language in a typical manner. These disorders can manifest as difficulties with grammar, vocabulary, sentence structure, word finding, following directions, and/or conversational skills.

Language development disorders can be receptive (difficulty understanding language), expressive (difficulty using language to communicate), or mixed (a combination of both). They can occur in isolation or as part of a broader neurodevelopmental disorder, such as autism spectrum disorder or intellectual disability.

The causes of language development disorders are varied and may include genetic factors, environmental influences, neurological conditions, hearing loss, or other medical conditions. It is important to note that language development disorders are not the result of low intelligence or lack of motivation; rather, they reflect a specific impairment in the brain's language processing systems.

Early identification and intervention for language development disorders can significantly improve outcomes and help individuals develop effective communication skills. Treatment typically involves speech-language therapy, which may be provided individually or in a group setting, and may involve strategies such as modeling correct language use, practicing targeted language skills, and using visual aids to support comprehension.

"Subject Headings" is not a medical term per se, but rather a term used in the field of library science and information management. Subject headings are standardized terms or phrases used to describe the subject or content of a document, such as a book, article, or research paper, in a consistent and controlled way. They help organize and retrieve information by providing a uniform vocabulary for indexing and searching.

In the medical field, subject headings may be used in databases like PubMed, Medline, and CINAHL to categorize and search for medical literature. For example, the National Library of Medicine's MeSH (Medical Subject Headings) is a controlled vocabulary used for indexing and searching biomedical literature. It includes headings for various medical concepts, such as diseases, treatments, anatomical structures, and procedures, which can be used to search for relevant articles in PubMed and other databases.

Sign language is not considered a medical term, but it is a visual-manual means of communication used by individuals who are deaf or hard of hearing. It combines hand shapes, orientation, and movement of the hands, arms, or body, along with facial expressions and lip patterns. Different sign languages exist in various countries and communities, such as American Sign Language (ASL) and British Sign Language (BSL).

However, I can provide a definition related to medical terminology that involves the use of gestures for communication purposes:

Gesture (in medical context): A bodily action or movement, often used to convey information or communicate. In some medical situations, healthcare professionals may use simple, predefined gestures to elicit responses from patients who have difficulty with verbal communication due to conditions like aphasia, dysarthria, or being in a coma. These gestures can be part of a more comprehensive system called "gesture-based communication" or "nonverbal communication."

For sign language specifically, you may consult resources related to linguistics, special education, or deaf studies for detailed definitions and descriptions.

An Electronic Health Record (EHR) is a digital version of a patient's medical history that is stored and maintained electronically rather than on paper. It contains comprehensive information about a patient's health status, including their medical history, medications, allergies, test results, immunization records, and other relevant health information. EHRs can be shared among authorized healthcare providers, which enables better coordination of care, improved patient safety, and more efficient delivery of healthcare services.

EHRs are designed to provide real-time, patient-centered records that make it easier for healthcare providers to access up-to-date and accurate information about their patients. They can also help reduce errors, prevent duplicative tests and procedures, and improve communication among healthcare providers. EHRs may include features such as clinical decision support tools, which can alert healthcare providers to potential drug interactions or other health risks based on a patient's medical history.

EHRs are subject to various regulations and standards to ensure the privacy and security of patients' health information. In the United States, for example, EHRs must comply with the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule, which sets national standards for the protection of personal health information.

"Terminology as a topic" in the context of medical education and practice refers to the study and use of specialized language and terms within the field of medicine. This includes understanding the meaning, origins, and appropriate usage of medical terminology in order to effectively communicate among healthcare professionals and with patients. It may also involve studying the evolution and cultural significance of medical terminology. The importance of "terminology as a topic" lies in promoting clear and accurate communication, which is essential for providing safe and effective patient care.

Medline is not a medical condition or term, but rather a biomedical bibliographic database, which is a component of the U.S. National Library of Medicine (NLM)'s PubMed system. It contains citations and abstracts from scientific literature in the fields of life sciences, biomedicine, and clinical medicine, with a focus on articles published in peer-reviewed journals. Medline covers a wide range of topics, including research articles, reviews, clinical trials, and case reports. The database is updated daily and provides access to over 26 million references from the years 1946 to the present. It's an essential resource for healthcare professionals, researchers, and students in the biomedical field.

Artificial Intelligence (AI) in the medical context refers to the simulation of human intelligence processes by machines, particularly computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction.

In healthcare, AI is increasingly being used to analyze large amounts of data, identify patterns, make decisions, and perform tasks that would normally require human intelligence. This can include tasks such as diagnosing diseases, recommending treatments, personalizing patient care, and improving clinical workflows.

Examples of AI in medicine include machine learning algorithms that analyze medical images to detect signs of disease, natural language processing tools that extract relevant information from electronic health records, and robot-assisted surgery systems that enable more precise and minimally invasive procedures.

'Abbreviations as Topic' in medical terms refers to the use and interpretation of abbreviated words or phrases that are commonly used in the field of medicine. These abbreviations can represent various concepts, such as medical conditions, treatments, procedures, diagnostic tests, and more.

Medical abbreviations are often used in clinical documentation, including patient records, progress notes, orders, and medication administration records. They help healthcare professionals communicate efficiently and effectively, reducing the need for lengthy descriptions and improving clarity in written communication.

However, medical abbreviations can also be a source of confusion and error if they are misinterpreted or used incorrectly. Therefore, it is essential to use standardized abbreviations that are widely recognized and accepted within the medical community. Additionally, healthcare professionals should always ensure that their use of abbreviations does not compromise patient safety or lead to misunderstandings in patient care.

Examples of commonly used medical abbreviations include:

* PT: Physical Therapy
* BP: Blood Pressure
* HR: Heart Rate
* Rx: Prescription
* NPO: Nothing by Mouth
* IV: Intravenous
* IM: Intramuscular
* COPD: Chronic Obstructive Pulmonary Disease
* MI: Myocardial Infarction (Heart Attack)
* Dx: Diagnosis

It is important to note that some medical abbreviations can have multiple meanings, and their interpretation may depend on the context in which they are used. Therefore, it is essential to use caution when interpreting medical abbreviations and seek clarification if necessary to ensure accurate communication and patient care.

Problem-Oriented Medical Records (PMR) is a system for organizing and documenting patient information in a structured and standardized format. It was introduced in the 1960s by Dr. Lawrence Weed as a way to improve the quality and efficiency of medical care.

The core component of PMR is the problem list, which is a comprehensive and prioritized list of the patient's current and past medical problems. Each problem is assigned a unique identifier, and all subsequent documentation related to that problem is linked to it. This allows for easy access to relevant information and facilitates continuity of care.

PMR also includes other sections such as the database, which contains information about the patient's history, physical examination findings, laboratory results, and other diagnostic tests; the progress notes, which document the assessment and management of the patient's problems over time; and the discharge summary, which summarizes the patient's hospital course and provides recommendations for follow-up care.

PMR is designed to promote clear communication, evidence-based decision making, and effective coordination of care among healthcare providers. It has been widely adopted in various settings, including hospitals, clinics, and electronic health records (EHR) systems.

RxNorm is a standardized nomenclature for clinical drugs produced by the US National Library of Medicine (NLM). It provides normalized names for medications, links its names to different drug delivery forms, and includes measures of the strengths of the drugs. RxNorm aims to represent the complex relationships between various medicinal products, including their ingredients, brand names, and generic counterparts. By providing a standardized vocabulary for clinical drugs, RxNorm facilitates safer medication prescribing, dispensing, and administration, as well as supports data analysis, research, and public health reporting.

"Dictionaries as Topic" is a medical subject heading (MeSH) that refers to the study or discussion of dictionaries as a reference source in the field of medicine. Dictionaries used in this context are specialized works that provide definitions and explanations of medical terms, concepts, and technologies. They serve as important tools for healthcare professionals, researchers, students, and patients to communicate effectively and accurately about health and disease.

Medical dictionaries can cover a wide range of topics, including anatomy, physiology, pharmacology, pathology, diagnostic procedures, treatment methods, and medical ethics. They may also provide information on medical eponyms, abbreviations, symbols, and units of measurement. Some medical dictionaries are general in scope, while others focus on specific areas of medicine or healthcare, such as nursing, dentistry, veterinary medicine, or alternative medicine.

The use of medical dictionaries can help to ensure that medical terminology is used consistently and correctly, which is essential for accurate diagnosis, treatment planning, and communication among healthcare providers and between providers and patients. Medical dictionaries can also be useful for non-medical professionals who need to understand medical terms in the context of their work, such as lawyers, journalists, and policymakers.

Crowdsourcing is not a medical term, but rather a general term used to describe the process of obtaining ideas, services, or content by soliciting contributions from a large number of people, typically via the internet. In a medical context, crowdsourcing may be used in research, clinical trials, or patient care to gather data, opinions, or solutions from a diverse group of individuals. For example, researchers may use crowdsourcing to gather data on the symptoms and experiences of patients with a particular condition, or clinicians may use it to get input on challenging diagnostic cases.

A User-Computer Interface (also known as Human-Computer Interaction) refers to the point at which a person (user) interacts with a computer system. This can include both hardware and software components, such as keyboards, mice, touchscreens, and graphical user interfaces (GUIs). The design of the user-computer interface is crucial in determining the usability and accessibility of a computer system for the user. A well-designed interface should be intuitive, efficient, and easy to use, minimizing the cognitive load on the user and allowing them to effectively accomplish their tasks.

Language therapy, also known as speech-language therapy, is a type of treatment aimed at improving an individual's communication and swallowing abilities. Speech-language pathologists (SLPs) or therapists provide this therapy to assess, diagnose, and treat a wide range of communication and swallowing disorders that can occur in people of all ages, from infants to the elderly.

Language therapy may involve working on various skills such as:

1. Expressive language: Improving the ability to express thoughts, needs, wants, and ideas through verbal, written, or other symbolic systems.
2. Receptive language: Enhancing the understanding of spoken or written language, including following directions and comprehending conversations.
3. Pragmatic or social language: Developing appropriate use of language in various social situations, such as turn-taking, topic maintenance, and making inferences.
4. Articulation and phonology: Correcting speech sound errors and improving overall speech clarity.
5. Voice and fluency: Addressing issues related to voice quality, volume, and pitch, as well as stuttering or stammering.
6. Literacy: Improving reading, writing, and spelling skills.
7. Swallowing: Evaluating and treating swallowing disorders (dysphagia) to ensure safe and efficient eating and drinking.

Language therapy often involves a combination of techniques, including exercises, drills, conversation practice, and the use of various therapeutic materials and technology. The goal of language therapy is to help individuals with communication disorders achieve optimal functional communication and swallowing abilities in their daily lives.

The Systematized Nomenclature of Medicine (SNOMED) is a systematically organized collection of medical terms that are used to describe medical diagnoses, findings, procedures, and other health-related concepts. It is a standardized terminology that is widely adopted in the field of healthcare and clinical research to facilitate accurate and consistent exchange of health information among different healthcare providers, institutions, and electronic health records (EHRs) systems.

SNOMED is designed to capture detailed clinical data and support effective clinical decision-making by providing a common language for describing and sharing clinical information. It includes over 350,000 concepts that are organized into hierarchies based on their relationships to each other. The hierarchical structure of SNOMED allows users to navigate through the terminology and find the most specific concept that describes a particular clinical phenomenon.

SNOMED is maintained by the International Health Terminology Standards Development Organization (IHTSDO), which is responsible for updating and expanding the terminology to reflect changes in medical knowledge and practice. SNOMED is used in many countries around the world, including the United States, Canada, Australia, and several European countries.

An algorithm is not a medical term, but rather a concept from computer science and mathematics. In the context of medicine, algorithms are often used to describe step-by-step procedures for diagnosing or managing medical conditions. These procedures typically involve a series of rules or decision points that help healthcare professionals make informed decisions about patient care.

For example, an algorithm for diagnosing a particular type of heart disease might involve taking a patient's medical history, performing a physical exam, ordering certain diagnostic tests, and interpreting the results in a specific way. By following this algorithm, healthcare professionals can ensure that they are using a consistent and evidence-based approach to making a diagnosis.

Algorithms can also be used to guide treatment decisions. For instance, an algorithm for managing diabetes might involve setting target blood sugar levels, recommending certain medications or lifestyle changes based on the patient's individual needs, and monitoring the patient's response to treatment over time.

Overall, algorithms are valuable tools in medicine because they help standardize clinical decision-making and ensure that patients receive high-quality care based on the latest scientific evidence.

A Database Management System (DBMS) is a software application that enables users to define, create, maintain, and manipulate databases. It provides a structured way to organize, store, retrieve, and manage data in a digital format. The DBMS serves as an interface between the database and the applications or users that access it, allowing for standardized interactions and data access methods. Common functions of a DBMS include data definition, data manipulation, data security, data recovery, and concurrent data access control. Examples of DBMS include MySQL, Oracle, Microsoft SQL Server, and MongoDB.

PubMed is not a medical condition or term, but rather a biomedical literature search engine and database maintained by the National Center for Biotechnology Information (NCBI), a division of the U.S. National Library of Medicine (NLM). It provides access to life sciences literature, including journal articles in medicine, nursing, dentistry, veterinary medicine, health care systems, and preclinical sciences.

PubMed contains more than 30 million citations and abstracts from MEDLINE, life science journals, and online books. Many of the citations include links to full-text articles on publishers' websites or through NCBI's DocSumo service. Researchers, healthcare professionals, students, and the general public use PubMed to find relevant and reliable information in the biomedical literature for research, education, and patient care purposes.

"Forms and Records Control" is not a recognized medical term or concept. However, in a broader healthcare context, "Records Control" typically refers to the systematic management and maintenance of patient records to ensure their accuracy, confidentiality, and accessibility. This includes establishing policies and procedures for creating, storing, retrieving, using, and disposing of records in compliance with applicable laws and regulations.

"Forms," on the other hand, are standardized documents used in healthcare settings to collect and record patient information. "Forms Control" may refer to the management and tracking of these forms to ensure they are up-to-date, compliant with relevant regulations, and accessible to authorized personnel. This can include developing and implementing processes for creating, revising, approving, distributing, and retiring healthcare forms.

In summary, "Forms and Records Control" in a healthcare context could be interpreted as the combined management of standardized forms used to collect patient information and the systematic maintenance of those records to ensure accuracy, confidentiality, and compliance with applicable laws and regulations.

An Expert System is a type of artificial intelligence (AI) program that emulates the decision-making ability of a human expert in a specific field or domain. It is designed to solve complex problems by using a set of rules, heuristics, and knowledge base derived from human expertise. The system can simulate the problem-solving process of a human expert, allowing it to provide advice, make recommendations, or diagnose problems in a similar manner. Expert systems are often used in fields such as medicine, engineering, finance, and law where specialized knowledge and experience are critical for making informed decisions.

The medical definition of 'Expert Systems' refers to AI programs that assist healthcare professionals in diagnosing and treating medical conditions, based on a large database of medical knowledge and clinical expertise. These systems can help doctors and other healthcare providers make more accurate diagnoses, recommend appropriate treatments, and provide patient education. They may also be used for research, training, and quality improvement purposes.

Expert systems in medicine typically use a combination of artificial intelligence techniques such as rule-based reasoning, machine learning, natural language processing, and pattern recognition to analyze medical data and provide expert advice. Examples of medical expert systems include MYCIN, which was developed to diagnose infectious diseases, and Internist-1, which assists in the diagnosis and management of internal medicine cases.

I'm sorry for any confusion, but "knowledge bases" is a general term that refers to structured collections of knowledge in a specific field or area, and it is not a medical term with a defined meaning in the medical field. Knowledge bases can be found in many fields, including medicine, and they typically take the form of databases or repositories of information that are used to store, organize, and retrieve knowledge. In the medical field, knowledge bases might include information about diseases, treatments, medications, and other medical topics. They can be used by healthcare professionals, researchers, and patients to access accurate and reliable information.

Psycholinguistics is not a medical term per se, but it is a subfield of both psychology and linguistics that explores how we understand, produce, and process language. It investigates the cognitive processes and mental representations involved in language use, such as word recognition, sentence comprehension, language production, language acquisition, and language disorders.

In medical contexts, psycholinguistic assessments may be used to evaluate individuals with communication difficulties due to neurological or developmental disorders, such as aphasia, dyslexia, or autism spectrum disorder. These assessments can help identify specific areas of impairment and inform treatment planning.

In the context of medicine, particularly in neurolinguistics and speech-language pathology, language is defined as a complex system of communication that involves the use of symbols (such as words, signs, or gestures) to express and exchange information. It includes various components such as phonology (sound systems), morphology (word structures), syntax (sentence structure), semantics (meaning), and pragmatics (social rules of use). Language allows individuals to convey their thoughts, feelings, and intentions, and to understand the communication of others. Disorders of language can result from damage to specific areas of the brain, leading to impairments in comprehension, production, or both.

Biological ontologies are formal representations of knowledge in the biological sciences, which consist of standardized vocabularies and relationships between them. They provide a way to represent and organize complex concepts and relationships in a machine-readable format, enabling computational analysis and integration of diverse biological data. Ontologies can capture various levels of biological organization, from molecular interactions to whole organisms and ecosystems. Examples of widely used biological ontologies include the Gene Ontology (GO) for molecular functions and processes, the Cell Ontology (CL) for cell types, and the Chemical Entities of Biological Interest (ChEBI) ontology for small molecules.

Automated Pattern Recognition in a medical context refers to the use of computer algorithms and artificial intelligence techniques to identify, classify, and analyze specific patterns or trends in medical data. This can include recognizing visual patterns in medical images, such as X-rays or MRIs, or identifying patterns in large datasets of physiological measurements or electronic health records.

The goal of automated pattern recognition is to assist healthcare professionals in making more accurate diagnoses, monitoring disease progression, and developing personalized treatment plans. By automating the process of pattern recognition, it can help reduce human error, increase efficiency, and improve patient outcomes.

Examples of automated pattern recognition in medicine include using machine learning algorithms to identify early signs of diabetic retinopathy in eye scans or detecting abnormal heart rhythms in electrocardiograms (ECGs). These techniques can also be used to predict patient risk based on patterns in their medical history, such as identifying patients who are at high risk for readmission to the hospital.

I am not aware of a widely accepted medical definition for the term "software," as it is more commonly used in the context of computer science and technology. Software refers to programs, data, and instructions that are used by computers to perform various tasks. It does not have direct relevance to medical fields such as anatomy, physiology, or clinical practice. If you have any questions related to medicine or healthcare, I would be happy to try to help with those instead!

Pathology is a significant branch of medical science that deals with the study of the nature of diseases, their causes, processes, development, and consequences. It involves the examination of tissues, organs, bodily fluids, and autopsies to diagnose disease and determine the course of treatment. Pathology can be divided into various sub-specialties such as anatomical pathology, clinical pathology, molecular pathology, and forensic pathology. Ultimately, pathology aims to understand the mechanisms of diseases and improve patient care through accurate diagnosis and effective treatment plans.

I must clarify that I'm here to provide information related to health, medicine, and symptoms. I don't provide definitions for literary devices such as "metaphor." However, I can tell you that in the context of medicine, metaphors are often used to help explain medical concepts to patients in a more understandable and relatable way. For example, a doctor might describe a leaky heart valve as "a gate that doesn't close properly, allowing blood to leak back." This is not a formal medical definition, but rather a figure of speech used to help patients better understand their condition.

Medical records are organized, detailed collections of information about a patient's health history, including their symptoms, diagnoses, treatments, medications, test results, and any other relevant data. These records are created and maintained by healthcare professionals during the course of providing medical care and serve as an essential tool for continuity, communication, and decision-making in healthcare. They may exist in paper form, electronic health records (EHRs), or a combination of both. Medical records also play a critical role in research, quality improvement, public health, reimbursement, and legal proceedings.

A Radiology Information System (RIS) is a type of healthcare software specifically designed to manage medical imaging data and related patient information. It serves as a centralized database and communication platform for radiology departments, allowing the integration, storage, retrieval, and sharing of patient records, orders, reports, images, and other relevant documents.

The primary functions of a RIS typically include:

1. Scheduling and tracking: Managing appointments, scheduling resources, and monitoring workflow within the radiology department.
2. Order management: Tracking and processing requests for imaging exams from referring physicians or other healthcare providers.
3. Image tracking: Monitoring the movement of images throughout the entire imaging process, from acquisition to reporting and storage.
4. Report generation: Assisting radiologists in creating structured, standardized reports based on the interpreted imaging studies.
5. Results communication: Sending finalized reports back to the referring physicians or other healthcare providers, often through integration with electronic health records (EHRs) or hospital information systems (HIS).
6. Data analytics: Providing tools for analyzing and reporting departmental performance metrics, such as turnaround times, equipment utilization, and patient satisfaction.
7. Compliance and security: Ensuring adherence to regulatory requirements related to data privacy, protection, and storage, while maintaining secure access controls for authorized users.

By streamlining these processes, a RIS helps improve efficiency, reduce errors, enhance communication, and support better patient care within radiology departments.

In a medical context, documentation refers to the process of recording and maintaining written or electronic records of a patient's health status, medical history, treatment plans, medications, and other relevant information. The purpose of medical documentation is to provide clear and accurate communication among healthcare providers, to support clinical decision-making, to ensure continuity of care, to meet legal and regulatory requirements, and to facilitate research and quality improvement initiatives.

Medical documentation typically includes various types of records such as:

1. Patient's demographic information, including name, date of birth, gender, and contact details.
2. Medical history, including past illnesses, surgeries, allergies, and family medical history.
3. Physical examination findings, laboratory and diagnostic test results, and diagnoses.
4. Treatment plans, including medications, therapies, procedures, and follow-up care.
5. Progress notes, which document the patient's response to treatment and any changes in their condition over time.
6. Consultation notes, which record communication between healthcare providers regarding a patient's care.
7. Discharge summaries, which provide an overview of the patient's hospital stay, including diagnoses, treatments, and follow-up plans.

Medical documentation must be clear, concise, accurate, and timely, and it should adhere to legal and ethical standards. Healthcare providers are responsible for maintaining the confidentiality of patients' medical records and ensuring that they are accessible only to authorized personnel.

I'm sorry for any confusion, but "Language Arts" is not a term used in medical definitions. Language arts is a term commonly used in education to refer to the academic study of reading, writing, speaking, and listening. It encompasses various subjects such as English, literature, grammar, creative writing, and communication skills. If you have any questions related to medical terminology or health-related topics, I would be happy to help with those!

Automatic Data Processing (ADP) is not a medical term, but a general business term that refers to the use of computers and software to automate and streamline administrative tasks and processes. In a medical context, ADP may be used in healthcare settings to manage electronic health records (EHRs), billing and coding, insurance claims processing, and other data-intensive tasks.

The goal of using ADP in healthcare is to improve efficiency, accuracy, and timeliness of administrative processes, while reducing costs and errors associated with manual data entry and management. By automating these tasks, healthcare providers can focus more on patient care and less on paperwork, ultimately improving the quality of care delivered to patients.

I'm sorry for any confusion, but "vocabulary" is a term related to language and communication, rather than medicine or healthcare. It refers to the words and phrases that a person knows and uses in their communication. If you have any questions related to medical terminology or health-related topics, I would be happy to help with those!

Medical Informatics, also known as Healthcare Informatics, is the scientific discipline that deals with the systematic processing and analysis of data, information, and knowledge in healthcare and biomedicine. It involves the development and application of theories, methods, and tools to create, acquire, store, retrieve, share, use, and reuse health-related data and knowledge for clinical, educational, research, and administrative purposes. Medical Informatics encompasses various areas such as bioinformatics, clinical informatics, consumer health informatics, public health informatics, and translational bioinformatics. It aims to improve healthcare delivery, patient outcomes, and biomedical research through the effective use of information technology and data management strategies.

I believe there may be some confusion in your question. "Writing" is a common term used to describe the act or process of creating written content, whether it's for literary, professional, or personal purposes. However, if you're asking for a medical term related to writing, perhaps you meant "graphomotor," which refers to the fine motor skills required to produce handwriting or signing one's name. If this is not what you were looking for, please clarify your question so I can provide a more accurate answer.

In the context of medicine, "narration" typically refers to the description or telling of a patient's history, symptoms, and course of illness. It is the process of recounting the important medical events and experiences related to a patient's health status. This information is usually gathered through interviews, physical examinations, and review of medical records. The resulting narrative can help healthcare providers understand the patient's condition, make informed decisions about diagnosis and treatment, and provide appropriate care. However, it's important to note that "narration" itself is not a medical term, but rather a general term used in many fields including medicine.

Speech recognition software, also known as voice recognition software, is a type of technology that converts spoken language into written text. It utilizes sophisticated algorithms and artificial intelligence to identify and transcribe spoken words, enabling users to interact with computers and digital devices using their voice rather than typing or touching the screen. This technology has various applications in healthcare, including medical transcription, patient communication, and hands-free documentation, which can help improve efficiency, accuracy, and accessibility for patients and healthcare professionals alike.

A medical dictionary is a reference book that contains definitions and explanations of medical terms and jargon. It serves as a useful tool for healthcare professionals, students, patients, and anyone else who needs to understand medical terminology. Medical dictionaries can include definitions of diseases, conditions, treatments, procedures, drugs, equipment, anatomy, and more. They may also provide pronunciation guides, etymologies, and abbreviations.

Medical dictionaries can be found in print or digital form, and some are specialized to cover specific areas of medicine, such as oncology, psychiatry, or surgery. Some medical dictionaries are also bilingual, providing translations of medical terms between different languages. Overall, a medical dictionary is an essential resource for anyone who needs to communicate effectively in the field of medicine.

A factual database in the medical context is a collection of organized and structured data that contains verified and accurate information related to medicine, healthcare, or health sciences. These databases serve as reliable resources for various stakeholders, including healthcare professionals, researchers, students, and patients, to access evidence-based information for making informed decisions and enhancing knowledge.

Examples of factual medical databases include:

1. PubMed: A comprehensive database of biomedical literature maintained by the US National Library of Medicine (NLM). It contains citations and abstracts from life sciences journals, books, and conference proceedings.
2. MEDLINE: A subset of PubMed, MEDLINE focuses on high-quality, peer-reviewed articles related to biomedicine and health. It is the primary component of the NLM's database and serves as a critical resource for healthcare professionals and researchers worldwide.
3. Cochrane Library: A collection of systematic reviews and meta-analyses focused on evidence-based medicine. The library aims to provide unbiased, high-quality information to support clinical decision-making and improve patient outcomes.
4. OVID: A platform that offers access to various medical and healthcare databases, including MEDLINE, Embase, and PsycINFO. It facilitates the search and retrieval of relevant literature for researchers, clinicians, and students.
5. ClinicalTrials.gov: A registry and results database of publicly and privately supported clinical studies conducted around the world. The platform aims to increase transparency and accessibility of clinical trial data for healthcare professionals, researchers, and patients.
6. UpToDate: An evidence-based, physician-authored clinical decision support resource that provides information on diagnosis, treatment, and prevention of medical conditions. It serves as a point-of-care tool for healthcare professionals to make informed decisions and improve patient care.
7. TRIP Database: A search engine designed to facilitate evidence-based medicine by providing quick access to high-quality resources, including systematic reviews, clinical guidelines, and practice recommendations.
8. National Guideline Clearinghouse (NGC): A database of evidence-based clinical practice guidelines and related documents developed through a rigorous review process. The NGC aims to provide clinicians, healthcare providers, and policymakers with reliable guidance for patient care.
9. DrugBank: A comprehensive, freely accessible online database containing detailed information about drugs, their mechanisms, interactions, and targets. It serves as a valuable resource for researchers, healthcare professionals, and students in the field of pharmacology and drug discovery.
10. Genetic Testing Registry (GTR): A database that provides centralized information about genetic tests, test developers, laboratories offering tests, and clinical validity and utility of genetic tests. It serves as a resource for healthcare professionals, researchers, and patients to make informed decisions regarding genetic testing.

A bibliographic database is a type of database that contains records of publications, such as books, articles, and conference proceedings. These records typically include bibliographic information, such as the title, author, publication date, and source of the publication. Some bibliographic databases also include abstracts or summaries of the publications, and many provide links to the full text of the publications if they are available online.

Bibliographic databases are used in a variety of fields, including academia, medicine, and industry, to locate relevant publications on a particular topic. They can be searched using keywords, author names, and other criteria. Some bibliographic databases are general, covering a wide range of topics, while others are specialized and focus on a specific subject area.

In the medical field, bibliographic databases such as MEDLINE and PubMed are widely used to search for articles related to biomedical research, clinical practice, and public health. These databases contain records of articles from thousands of biomedical journals and can be searched using keywords, MeSH (Medical Subject Headings) terms, and other criteria.

Medical Subject Headings (MeSH) is a controlled vocabulary thesaurus produced by the U.S. National Library of Medicine (NLM). It is used to index, catalog, and search for biomedical and health-related information and documents, such as journal articles and books. MeSH terms represent a consistent and standardized way to describe and categorize biomedical concepts, allowing for more precise and effective searching and retrieval of relevant information. The MeSH hierarchy includes descriptors for various categories including diseases, chemicals, drugs, anatomical parts, physiological functions, and procedures, among others.

I'm sorry for any confusion, but "hypermedia" is not a term that has a specific medical definition. Hypermedia is a general term used in information technology and computing to describe a non-linear medium of information that includes graphics, audio, video, text, and hyperlinks. It allows users to navigate through the information in a flexible, non-sequential manner by clicking on hyperlinks that connect related pieces of information.

If you have any questions about medical terminology or concepts, I would be happy to help!

A "periodical" in the context of medicine typically refers to a type of publication that is issued regularly, such as on a monthly or quarterly basis. These publications include peer-reviewed journals, magazines, and newsletters that focus on medical research, education, and practice. They may contain original research articles, review articles, case reports, editorials, letters to the editor, and other types of content related to medical science and clinical practice.

As a "Topic," periodicals in medicine encompass various aspects such as their role in disseminating new knowledge, their impact on clinical decision-making, their quality control measures, and their ethical considerations. Medical periodicals serve as a crucial resource for healthcare professionals, researchers, students, and other stakeholders to stay updated on the latest developments in their field and to share their findings with others.

A "Research Report" in the medical context is a comprehensive and systematic documentation of the entire process, findings, and conclusions of a scientific research study. It typically includes an abstract, introduction, methodology, results, discussion, and conclusion sections. The report may also contain information about the funding sources, potential conflicts of interest, and ethical considerations related to the research. The purpose of a research report is to allow other researchers to critically evaluate the study, replicate its findings, and build upon its knowledge. It should adhere to strict standards of scientific reporting and be written in a clear, concise, and objective manner.

Thoracic radiography is a type of diagnostic imaging that involves using X-rays to produce images of the chest, including the lungs, heart, bronchi, great vessels, and the bones of the spine and chest wall. It is a commonly used tool in the diagnosis and management of various respiratory, cardiovascular, and thoracic disorders such as pneumonia, lung cancer, heart failure, and rib fractures.

During the procedure, the patient is positioned between an X-ray machine and a cassette containing a film or digital detector. The X-ray beam is directed at the chest, and the resulting image is captured on the film or detector. The images produced can help identify any abnormalities in the structure or function of the organs within the chest.

Thoracic radiography may be performed as a routine screening test for certain conditions, such as lung cancer, or it may be ordered when a patient presents with symptoms suggestive of a respiratory or cardiovascular disorder. It is a safe and non-invasive procedure that can provide valuable information to help guide clinical decision making and improve patient outcomes.

Patient discharge is a medical term that refers to the point in time when a patient is released from a hospital or other healthcare facility after receiving treatment. This process typically involves the physician or healthcare provider determining that the patient's condition has improved enough to allow them to continue their recovery at home or in another appropriate setting.

The discharge process may include providing the patient with instructions for ongoing care, such as medication regimens, follow-up appointments, and activity restrictions. The healthcare team may also provide educational materials and resources to help patients and their families manage their health conditions and prevent complications.

It is important for patients and their families to understand and follow the discharge instructions carefully to ensure a smooth transition back to home or another care setting and to promote continued recovery and good health.

Computational biology is a branch of biology that uses mathematical and computational methods to study biological data, models, and processes. It involves the development and application of algorithms, statistical models, and computational approaches to analyze and interpret large-scale molecular and phenotypic data from genomics, transcriptomics, proteomics, metabolomics, and other high-throughput technologies. The goal is to gain insights into biological systems and processes, develop predictive models, and inform experimental design and hypothesis testing in the life sciences. Computational biology encompasses a wide range of disciplines, including bioinformatics, systems biology, computational genomics, network biology, and mathematical modeling of biological systems.

Echolalia is a term used in the field of medicine, specifically in neurology and psychology. It refers to the repetition of words or phrases spoken by another person, mimicking their speech in a near identical manner. This behavior is often observed in individuals with developmental disorders such as autism spectrum disorder (ASD).

Echolalia can be either immediate or delayed. Immediate echolalia occurs when an individual repeats the words or phrases immediately after they are spoken by someone else. Delayed echolalia, on the other hand, involves the repetition of words or phrases that were heard at an earlier time.

Echolalia is not necessarily a pathological symptom and can be a normal part of language development in young children who are learning to speak. However, when it persists beyond the age of 3-4 years or occurs in older individuals with developmental disorders, it may indicate difficulties with initiating spontaneous speech or forming original thoughts and ideas.

In some cases, echolalia can serve as a communication tool for individuals with ASD who have limited verbal abilities. By repeating words or phrases that they have heard before, they may be able to convey their needs or emotions in situations where they are unable to generate appropriate language on their own.

Natural Killer (NK) cells are a type of lymphocyte, which are large granular innate immune cells that play a crucial role in the host's defense against viral infections and malignant transformations. They do not require prior sensitization to target and destroy abnormal cells, such as virus-infected cells or tumor cells. NK cells recognize their targets through an array of germline-encoded activating and inhibitory receptors that detect the alterations in the cell surface molecules of potential targets. Upon activation, NK cells release cytotoxic granules containing perforins and granzymes to induce target cell apoptosis, and they also produce a variety of cytokines and chemokines to modulate immune responses. Overall, natural killer cells serve as a critical component of the innate immune system, providing rapid and effective responses against infected or malignant cells.

Medication systems refer to the organizational and operational structures, processes, and technologies that are put in place to ensure the safe and effective use of medications in healthcare settings. These systems encompass all aspects of medication management, including prescribing, transcribing, dispensing, administering, and monitoring. They are designed to minimize errors, improve patient outcomes, and reduce costs associated with medication-related harm.

Medication systems may include various components such as:

1. Medication ordering and documentation systems that standardize the way medications are prescribed and documented in the medical record.
2. Computerized physician order entry (CPOE) systems that allow providers to enter medication orders electronically, reducing errors associated with handwritten orders.
3. Pharmacy information systems that manage medication inventory, track medication use, and ensure the accuracy of dispensed medications.
4. Medication administration records (MARs) that document the medications administered to each patient, including the dose, route, and time of administration.
5. Automated dispensing systems that allow medications to be dispensed directly to patients or medication carts, reducing errors associated with manual handling of medications.
6. Smart infusion pumps that incorporate safety features such as dose error reduction software and drug libraries to prevent medication errors during infusion therapy.
7. Medication reconciliation processes that ensure accurate and up-to-date medication lists are maintained for each patient, reducing the risk of medication errors during transitions of care.
8. Clinical decision support systems that provide alerts and reminders to providers regarding potential drug-drug interactions, dosing errors, and other medication-related risks.
9. Patient education materials that provide clear and concise information about medications, including dosage instructions, side effects, and storage requirements.
10. Performance improvement processes that monitor medication use and outcomes, identify areas for improvement, and implement changes to the medication system as needed.

Anatomy is the branch of biology that deals with the study of the structure of organisms and their parts. In medicine, anatomy is the detailed study of the structures of the human body and its organs. It can be divided into several subfields, including:

1. Gross anatomy: Also known as macroscopic anatomy, this is the study of the larger structures of the body, such as the organs and organ systems, using techniques such as dissection and observation.
2. Histology: This is the study of tissues at the microscopic level, including their structure, composition, and function.
3. Embryology: This is the study of the development of the embryo and fetus from conception to birth.
4. Neuroanatomy: This is the study of the structure and organization of the nervous system, including the brain and spinal cord.
5. Comparative anatomy: This is the study of the structures of different species and how they have evolved over time.

Anatomy is a fundamental subject in medical education, as it provides the basis for understanding the function of the human body and the underlying causes of disease.

In the context of medical and clinical psychology, particularly in the field of applied behavior analysis (ABA), "verbal behavior" is a term used to describe the various functions or purposes of spoken language. It was first introduced by the psychologist B.F. Skinner in his 1957 book "Verbal Behavior."

Skinner proposed that verbal behavior could be classified into several categories based on its function, including:

1. Mand: A verbal operant in which a person requests or demands something from another person. For example, saying "I would like a glass of water" is a mand.
2. Tact: A verbal operant in which a person describes or labels something in their environment. For example, saying "That's a red apple" is a tact.
3. Echoic: A verbal operant in which a person repeats or imitates what they have heard. For example, saying "Hello" after someone says hello to you is an echoic.
4. Intraverbal: A verbal operant in which a person responds to another person's verbal behavior with their own verbal behavior, without simply repeating or imitating what they have heard. For example, answering a question like "What's the capital of France?" is an intraverbal.
5. Textual: A verbal operant in which a person reads or writes text. For example, reading a book or writing a letter are textual.

Understanding the function of verbal behavior can be helpful in assessing and treating communication disorders, such as those seen in autism spectrum disorder (ASD). By identifying the specific functions of a child's verbal behavior, therapists can develop targeted interventions to help them communicate more effectively.

Computer-assisted diagnosis (CAD) is the use of computer systems to aid in the diagnostic process. It involves the use of advanced algorithms and data analysis techniques to analyze medical images, laboratory results, and other patient data to help healthcare professionals make more accurate and timely diagnoses. CAD systems can help identify patterns and anomalies that may be difficult for humans to detect, and they can provide second opinions and flag potential errors or uncertainties in the diagnostic process.

CAD systems are often used in conjunction with traditional diagnostic methods, such as physical examinations and patient interviews, to provide a more comprehensive assessment of a patient's health. They are commonly used in radiology, pathology, cardiology, and other medical specialties where imaging or laboratory tests play a key role in the diagnostic process.

While CAD systems can be very helpful in the diagnostic process, they are not infallible and should always be used as a tool to support, rather than replace, the expertise of trained healthcare professionals. It's important for medical professionals to use their clinical judgment and experience when interpreting CAD results and making final diagnoses.

A diagnosis is the process of determining a disease or condition based on the patient's symptoms, medical history, and diagnostic tests. It is the conclusion reached by a healthcare professional after evaluating all available information about the patient's health. A diagnosis can be simple or complex, depending on the presenting symptoms and the underlying cause.

The process of making a diagnosis typically involves taking a thorough medical history, performing a physical examination, and ordering diagnostic tests such as blood tests, imaging studies, or genetic testing. The results of these tests are then analyzed to determine the most likely cause of the patient's symptoms. In some cases, a definitive diagnosis may not be possible, and the healthcare professional may use a process of elimination to narrow down the list of possible causes.

Once a diagnosis is made, the healthcare professional can develop an appropriate treatment plan for the patient. Accurate diagnosis is essential for effective treatment, as it allows healthcare professionals to target the underlying cause of the patient's symptoms and avoid unnecessary or ineffective treatments.

I'm sorry for any confusion, but "Internet" is a term that pertains to the global network of interconnected computers and servers that enable the transmission and reception of data via the internet protocol (IP). It is not a medical term and does not have a specific medical definition. If you have any questions related to medicine or health, I'd be happy to try to help answer them for you!

The term "Theoretical Models" is used in various scientific fields, including medicine, to describe a representation of a complex system or phenomenon. It is a simplified framework that explains how different components of the system interact with each other and how they contribute to the overall behavior of the system. Theoretical models are often used in medical research to understand and predict the outcomes of diseases, treatments, or public health interventions.

A theoretical model can take many forms, such as mathematical equations, computer simulations, or conceptual diagrams. It is based on a set of assumptions and hypotheses about the underlying mechanisms that drive the system. By manipulating these variables and observing the effects on the model's output, researchers can test their assumptions and generate new insights into the system's behavior.

Theoretical models are useful for medical research because they allow scientists to explore complex systems in a controlled and systematic way. They can help identify key drivers of disease or treatment outcomes, inform the design of clinical trials, and guide the development of new interventions. However, it is important to recognize that theoretical models are simplifications of reality and may not capture all the nuances and complexities of real-world systems. Therefore, they should be used in conjunction with other forms of evidence, such as experimental data and observational studies, to inform medical decision-making.

A Hospital Information System (HIS) is a comprehensive, integrated set of software solutions that support the management and operation of a hospital or healthcare facility. It typically includes various modules such as:

1. Electronic Health Record (EHR): A digital version of a patient's paper chart that contains all of their medical history from one or multiple providers.
2. Computerized Physician Order Entry (CPOE): A system that allows physicians to enter, modify, review, and communicate orders for tests, medications, and other treatments electronically.
3. Pharmacy Information System: A system that manages the medication use process, including ordering, dispensing, administering, and monitoring of medications.
4. Laboratory Information System (LIS): A system that automates and manages the laboratory testing process, from order entry to result reporting.
5. Radiology Information System (RIS): A system that manages medical imaging data, including scheduling, image acquisition, storage, and retrieval.
6. Picture Archiving and Communication System (PACS): A system that stores, distributes, and displays medical images from various modalities such as X-ray, CT, MRI, etc.
7. Admission, Discharge, and Transfer (ADT) system: A system that manages patient registration, scheduling, and tracking of their progress through the hospital.
8. Financial Management System: A system that handles billing, coding, and reimbursement processes.
9. Materials Management System: A system that tracks inventory, supply chain, and logistics operations within a healthcare facility.
10. Nursing Documentation System: A system that supports the documentation of nursing care, including assessments, interventions, and outcomes.

These systems are designed to improve the efficiency, quality, and safety of patient care by facilitating communication, coordination, and data sharing among healthcare providers and departments.

A computer system is a collection of hardware and software components that work together to perform specific tasks. This includes the physical components such as the central processing unit (CPU), memory, storage devices, and input/output devices, as well as the operating system and application software that run on the hardware. Computer systems can range from small, embedded systems found in appliances and devices, to large, complex networks of interconnected computers used for enterprise-level operations.

In a medical context, computer systems are often used for tasks such as storing and retrieving electronic health records (EHRs), managing patient scheduling and billing, performing diagnostic imaging and analysis, and delivering telemedicine services. These systems must adhere to strict regulatory standards, such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States, to ensure the privacy and security of sensitive medical information.

In the context of medicine, classification refers to the process of categorizing or organizing diseases, disorders, injuries, or other health conditions based on their characteristics, symptoms, causes, or other factors. This helps healthcare professionals to understand, diagnose, and treat various medical conditions more effectively.

There are several well-known classification systems in medicine, such as:

1. The International Classification of Diseases (ICD) - developed by the World Health Organization (WHO), it is used worldwide for mortality and morbidity statistics, reimbursement systems, and automated decision support in health care. This system includes codes for diseases, signs and symptoms, abnormal findings, social circumstances, and external causes of injury or diseases.
2. The Diagnostic and Statistical Manual of Mental Disorders (DSM) - published by the American Psychiatric Association, it provides a standardized classification system for mental health disorders to improve communication between mental health professionals, facilitate research, and guide treatment.
3. The International Classification of Functioning, Disability and Health (ICF) - developed by the WHO, this system focuses on an individual's functioning and disability rather than solely on their medical condition. It covers body functions and structures, activities, and participation, as well as environmental and personal factors that influence a person's life.
4. The TNM Classification of Malignant Tumors - created by the Union for International Cancer Control (UICC), it is used to describe the anatomical extent of cancer, including the size of the primary tumor (T), involvement of regional lymph nodes (N), and distant metastasis (M).

These classification systems help medical professionals communicate more effectively about patients' conditions, make informed treatment decisions, and track disease trends over time.

I must clarify that there is no specific medical definition for "Software Design." Software design is a term used in the field of software engineering and development, which includes the creation of detailed plans, schemas, and models that describe how a software system or application should be constructed and implemented. This process involves various activities such as defining the architecture, components, modules, interfaces, data structures, and algorithms required to build the software system.

However, in the context of medical software or healthcare applications, software design would still refer to the planning and structuring of the software system but with a focus on addressing specific needs and challenges within the medical domain. This might include considerations for data privacy and security, regulatory compliance (such as HIPAA or GDPR), integration with existing health IT systems, user experience (UX) design for healthcare professionals and patients, and evidence-based decision support features.

Decision Support Systems (DSS), Clinical are interactive computer-based information systems that help health care professionals and patients make informed clinical decisions. These systems use patient-specific data and clinical knowledge to generate patient-centered recommendations. They are designed to augment the decision-making abilities of clinicians, providing evidence-based suggestions while allowing for the integration of professional expertise, patient preferences, and values. Clinical DSS can support various aspects of healthcare delivery, including diagnosis, treatment planning, resource allocation, and quality improvement. They may incorporate a range of technologies, such as artificial intelligence, machine learning, and data analytics, to facilitate the processing and interpretation of complex clinical information.

Reproducibility of results in a medical context refers to the ability to obtain consistent and comparable findings when a particular experiment or study is repeated, either by the same researcher or by different researchers, following the same experimental protocol. It is an essential principle in scientific research that helps to ensure the validity and reliability of research findings.

In medical research, reproducibility of results is crucial for establishing the effectiveness and safety of new treatments, interventions, or diagnostic tools. It involves conducting well-designed studies with adequate sample sizes, appropriate statistical analyses, and transparent reporting of methods and findings to allow other researchers to replicate the study and confirm or refute the results.

The lack of reproducibility in medical research has become a significant concern in recent years, as several high-profile studies have failed to produce consistent findings when replicated by other researchers. This has led to increased scrutiny of research practices and a call for greater transparency, rigor, and standardization in the conduct and reporting of medical research.

Comprehension, in a medical context, usually refers to the ability to understand and interpret spoken or written language, as well as gestures and expressions. It is a key component of communication and cognitive functioning. Difficulties with comprehension can be a symptom of various neurological conditions, such as aphasia (a disorder caused by damage to the language areas of the brain), learning disabilities, or dementia. Assessment of comprehension is often part of neuropsychological evaluations and speech-language pathology assessments.

I'm sorry for any confusion, but "Systems Integration" is not a medical term per se. It is a term more commonly used in the fields of engineering, computer science, and information technology. However, I can provide you with a general definition:

Systems Integration refers to the process of combining different sub-systems or components into a single, cohesive system to allow seamless communication and data exchange between them. This integration aims to improve efficiency, performance, and overall functionality by unifying various standalone systems into an interconnected network that behaves as a unified whole.

In the context of healthcare, systems integration can be applied to merge different electronic health record (EHR) systems, medical devices, or other healthcare technologies to create a comprehensive, interoperable healthcare information system. This facilitates better care coordination, data sharing, and decision-making among healthcare providers, ultimately enhancing patient outcomes and satisfaction.

The International Classification of Diseases (ICD) is a standardized system for classifying and coding mortality and morbidity data, established by the World Health Organization (WHO). It provides a common language and framework for health professionals, researchers, and policymakers to share and compare health-related information across countries and regions.

The ICD codes are used to identify diseases, injuries, causes of death, and other health conditions. The classification includes categories for various body systems, mental disorders, external causes of injury and poisoning, and factors influencing health status. It also includes a section for symptoms, signs, and abnormal clinical and laboratory findings.

The ICD is regularly updated to incorporate new scientific knowledge and changing health needs. The most recent version, ICD-11, was adopted by the World Health Assembly in May 2019 and will come into effect on January 1, 2022. It includes significant revisions and expansions in several areas, such as mental, behavioral, neurological disorders, and conditions related to sexual health.

In summary, the International Classification of Diseases (ICD) is a globally recognized system for classifying and coding diseases, injuries, causes of death, and other health-related information, enabling standardized data collection, comparison, and analysis across countries and regions.

'Schizophrenic language' is not a formal medical term, but the concept refers to the unusual and often disturbed patterns of speech that can be observed in individuals with schizophrenia. These language abnormalities are considered one of the positive symptoms of schizophrenia and can include:

1. **Word Salad (Incoherent Speech)**: This is when a person's speech becomes disorganized, fragmented, and lacks logical or understandable connections between words, phrases, or sentences. It may seem like the individual is randomly stringing together words without any clear meaning.

2. **Neologisms (Made-Up Words)**: These are new words or phrases that have been invented by the individual. They may be understandable only to the person using them.

3. **Tangentiality (Straying Off Topic)**: This is when a person's responses are indirect and unrelated to the topic being discussed, although they may start off on topic. The speaker may stray further and further from the original point until they are no longer discussing it at all.

4. **Perseveration (Persistent Repetition)**: This is when a person repeats certain words, phrases, or ideas over and over again, even when they are not relevant to the conversation.

5. **Illogical Thinking/Conclusions**: A person's thoughts may not follow a logical sequence, leading to illogical conclusions or statements that do not make sense in the context of the conversation.

6. **Thought Disorder**: This is a broader term that includes various disturbances in thinking and thought processes, which can then manifest as abnormalities in speech.

It's important to note that these symptoms can vary widely from person to person, and not everyone with schizophrenia will experience all of them. Furthermore, these symptoms should be evaluated and diagnosed by a qualified mental health professional.

Speech is the vocalized form of communication using sounds and words to express thoughts, ideas, and feelings. It involves the articulation of sounds through the movement of muscles in the mouth, tongue, and throat, which are controlled by nerves. Speech also requires respiratory support, phonation (vocal cord vibration), and prosody (rhythm, stress, and intonation).

Speech is a complex process that develops over time in children, typically beginning with cooing and babbling sounds in infancy and progressing to the use of words and sentences by around 18-24 months. Speech disorders can affect any aspect of this process, including articulation, fluency, voice, and language.

In a medical context, speech is often evaluated and treated by speech-language pathologists who specialize in diagnosing and managing communication disorders.

Communication barriers in a medical context refer to any factors that prevent or hinder the effective exchange of information between healthcare providers and patients, or among healthcare professionals themselves. These barriers can lead to misunderstandings, errors, and poor patient outcomes. Common communication barriers include:

1. Language differences: When patients and healthcare providers do not speak the same language, it can lead to miscommunication and errors in diagnosis and treatment.
2. Cultural differences: Cultural beliefs and values can affect how patients perceive and communicate their symptoms and concerns, as well as how healthcare providers deliver care.
3. Literacy levels: Low health literacy can make it difficult for patients to understand medical information, follow treatment plans, and make informed decisions about their care.
4. Disability: Patients with hearing or vision impairments, speech disorders, or cognitive impairments may face unique communication challenges that require accommodations and specialized communication strategies.
5. Emotional factors: Patients who are anxious, stressed, or in pain may have difficulty communicating effectively, and healthcare providers may be less likely to listen actively or ask open-ended questions.
6. Power dynamics: Hierarchical relationships between healthcare providers and patients can create power imbalances that discourage patients from speaking up or asking questions.
7. Noise and distractions: Environmental factors such as noise, interruptions, and distractions can make it difficult for patients and healthcare providers to hear, focus, and communicate effectively.

Effective communication is critical in healthcare settings, and addressing communication barriers requires a multifaceted approach that includes training for healthcare providers, language services for limited English proficient patients, and accommodations for patients with disabilities.

Phonetics is not typically considered a medical term, but rather a branch of linguistics that deals with the sounds of human speech. It involves the study of how these sounds are produced, transmitted, and received, as well as how they are used to convey meaning in different languages. However, there can be some overlap between phonetics and certain areas of medical research, such as speech-language pathology or audiology, which may study the production, perception, and disorders of speech sounds for diagnostic or therapeutic purposes.

Medical history taking is the process of obtaining and documenting a patient's health information through a series of questions and observations. It is a critical component of the medical assessment and helps healthcare providers understand the patient's current health status, past medical conditions, medications, allergies, lifestyle habits, and family medical history.

The information gathered during medical history taking is used to make informed decisions about diagnosis, treatment, and management plans for the patient's care. The process typically includes asking open-ended questions, actively listening to the patient's responses, clarifying any uncertainties, and documenting the findings in a clear and concise manner.

Medical history taking can be conducted in various settings, including hospitals, clinics, or virtual consultations, and may be performed by physicians, nurses, or other healthcare professionals. It is essential to ensure that medical history taking is conducted in a private and confidential setting to protect the patient's privacy and maintain trust in the provider-patient relationship.

Biomedical research is a branch of scientific research that involves the study of biological processes and diseases in order to develop new treatments and therapies. This type of research often involves the use of laboratory techniques, such as cell culture and genetic engineering, as well as clinical trials in humans. The goal of biomedical research is to advance our understanding of how living organisms function and to find ways to prevent and treat various medical conditions. It encompasses a wide range of disciplines, including molecular biology, genetics, immunology, pharmacology, and neuroscience, among others. Ultimately, the aim of biomedical research is to improve human health and well-being.

Deafness is a hearing loss that is so severe that it results in significant difficulty in understanding or comprehending speech, even when using hearing aids. It can be congenital (present at birth) or acquired later in life due to various causes such as disease, injury, infection, exposure to loud noises, or aging. Deafness can range from mild to profound and may affect one ear (unilateral) or both ears (bilateral). In some cases, deafness may be accompanied by tinnitus, which is the perception of ringing or other sounds in the ears.

Deaf individuals often use American Sign Language (ASL) or other forms of sign language to communicate. Some people with less severe hearing loss may benefit from hearing aids, cochlear implants, or other assistive listening devices. Deafness can have significant social, educational, and vocational implications, and early intervention and appropriate support services are critical for optimal development and outcomes.

I'm sorry for any confusion, but "reading" is not a term that has a specific medical definition. Reading is the activity or process of deciphering and understanding written words or text. It is a fundamental skill in language acquisition and communication, and is not typically used in a medical context unless there is a concern related to reading difficulties or disorders, such as dyslexia. If you have any questions related to medical terminology or health concerns, I'd be happy to try to help answer those for you!

A computer is a programmable electronic device that can store, retrieve, and process data. It is composed of several components including:

1. Hardware: The physical components of a computer such as the central processing unit (CPU), memory (RAM), storage devices (hard drive or solid-state drive), and input/output devices (monitor, keyboard, and mouse).
2. Software: The programs and instructions that are used to perform specific tasks on a computer. This includes operating systems, applications, and utilities.
3. Input: Devices or methods used to enter data into a computer, such as a keyboard, mouse, scanner, or digital camera.
4. Processing: The function of the CPU in executing instructions and performing calculations on data.
5. Output: The results of processing, which can be displayed on a monitor, printed on paper, or saved to a storage device.

Computers come in various forms and sizes, including desktop computers, laptops, tablets, and smartphones. They are used in a wide range of applications, from personal use for communication, entertainment, and productivity, to professional use in fields such as medicine, engineering, finance, and education.

Cultural evolution is a term used to describe the process of change and development in human culture over time. It refers to the way in which cultural traits, practices, beliefs, and technologies spread, change, and evolve within and between populations. Cultural evolution is influenced by various factors such as demographic changes, migration, innovation, selection, and diffusion.

The study of cultural evolution draws on insights from anthropology, sociology, psychology, archaeology, linguistics, and other disciplines to understand the patterns and dynamics of cultural change. It emphasizes the importance of understanding culture as a complex adaptive system that evolves through processes of variation, selection, and transmission.

Cultural evolution is often studied using comparative methods, which involve comparing similarities and differences in cultural traits across different populations or time periods. This allows researchers to identify patterns of cultural change and infer the underlying mechanisms that drive them. Some researchers also use mathematical models and computational simulations to study cultural evolution, allowing them to explore the dynamics of cultural change in a more controlled and systematic way.

Overall, the study of cultural evolution seeks to provide a deeper understanding of how human cultures have evolved over time, and how they continue to adapt and change in response to changing social, environmental, and technological conditions.

A disease is a condition that impairs normal functioning and causes harm to the body. It is typically characterized by a specific set of symptoms and may be caused by genetic, environmental, or infectious agents. A disease can also be described as a disorder of structure or function in an organism that produces specific signs or symptoms. Diseases can range from minor ones, like the common cold, to serious illnesses, such as heart disease or cancer. They can also be acute, with a sudden onset and short duration, or chronic, lasting for a long period of time. Ultimately, a disease is any deviation from normal homeostasis that causes harm to an organism.

A genetic database is a type of biomedical or health informatics database that stores and organizes genetic data, such as DNA sequences, gene maps, genotypes, haplotypes, and phenotype information. These databases can be used for various purposes, including research, clinical diagnosis, and personalized medicine.

There are different types of genetic databases, including:

1. Genomic databases: These databases store whole genome sequences, gene expression data, and other genomic information. Examples include the National Center for Biotechnology Information's (NCBI) GenBank, the European Nucleotide Archive (ENA), and the DNA Data Bank of Japan (DDBJ).
2. Gene databases: These databases contain information about specific genes, including their location, function, regulation, and evolution. Examples include the Online Mendelian Inheritance in Man (OMIM) database, the Universal Protein Resource (UniProt), and the Gene Ontology (GO) database.
3. Variant databases: These databases store information about genetic variants, such as single nucleotide polymorphisms (SNPs), insertions/deletions (INDELs), and copy number variations (CNVs). Examples include the Database of Single Nucleotide Polymorphisms (dbSNP), the Catalogue of Somatic Mutations in Cancer (COSMIC), and the International HapMap Project.
4. Clinical databases: These databases contain genetic and clinical information about patients, such as their genotype, phenotype, family history, and response to treatments. Examples include the ClinVar database, the Pharmacogenomics Knowledgebase (PharmGKB), and the Genetic Testing Registry (GTR).
5. Population databases: These databases store genetic information about different populations, including their ancestry, demographics, and genetic diversity. Examples include the 1000 Genomes Project, the Human Genome Diversity Project (HGDP), and the Allele Frequency Net Database (AFND).

Genetic databases can be publicly accessible or restricted to authorized users, depending on their purpose and content. They play a crucial role in advancing our understanding of genetics and genomics, as well as improving healthcare and personalized medicine.

Speech disorders refer to a group of conditions in which a person has difficulty producing or articulating sounds, words, or sentences in a way that is understandable to others. These disorders can be caused by various factors such as developmental delays, neurological conditions, hearing loss, structural abnormalities, or emotional issues.

Speech disorders may include difficulties with:

* Articulation: the ability to produce sounds correctly and clearly.
* Phonology: the sound system of language, including the rules that govern how sounds are combined and used in words.
* Fluency: the smoothness and flow of speech, including issues such as stuttering or cluttering.
* Voice: the quality, pitch, and volume of the spoken voice.
* Resonance: the way sound is produced and carried through the vocal tract, which can affect the clarity and quality of speech.

Speech disorders can impact a person's ability to communicate effectively, leading to difficulties in social situations, academic performance, and even employment opportunities. Speech-language pathologists are trained to evaluate and treat speech disorders using various evidence-based techniques and interventions.

Speech production measurement is the quantitative analysis and assessment of various parameters and characteristics of spoken language, such as speech rate, intensity, duration, pitch, and articulation. These measurements can be used to diagnose and monitor speech disorders, evaluate the effectiveness of treatment, and conduct research in fields such as linguistics, psychology, and communication disorders. Speech production measurement tools may include specialized software, hardware, and techniques for recording, analyzing, and visualizing speech data.

I'm sorry for any confusion, but "United States" is a geopolitical entity, specifically the name of the country consisting of 50 states, and it is not a medical term or concept. Therefore, I can't provide a medical definition for it. If you have any questions related to health, medicine, or biology, I would be happy to try to help answer those!

A decision tree is a graphical representation of possible solutions to a decision based on certain conditions. It is a predictive modeling tool commonly used in statistics, data mining, and machine learning. In the medical field, decision trees can be used for clinical decision-making and predicting patient outcomes based on various factors such as symptoms, test results, or demographic information.

In a decision tree, each internal node represents a feature or attribute, and each branch represents a possible value or outcome of that feature. The leaves of the tree represent the final decisions or predictions. Decision trees are constructed by recursively partitioning the data into subsets based on the most significant attributes until a stopping criterion is met.

Decision trees can be used for both classification and regression tasks, making them versatile tools in medical research and practice. They can help healthcare professionals make informed decisions about patient care, identify high-risk patients, and develop personalized treatment plans. However, it's important to note that decision trees are only as good as the data they are trained on, and their accuracy may be affected by biases or limitations in the data.

I'm happy to help! However, I must clarify that "information dissemination" is not a medical term per se, but rather a general term used in various fields, including healthcare and medicine. It refers to the process of spreading or distributing information to a specific audience or the public.

In the context of medicine and healthcare, information dissemination often relates to sharing clinical guidelines, research findings, public health messages, or patient education materials with healthcare professionals, patients, or the general public. This can occur through various channels, such as scientific conferences, peer-reviewed journals, newsletters, websites, social media platforms, and other communication methods.

The goal of information dissemination in medicine is to ensure that accurate, evidence-based, and up-to-date information reaches the intended audience, ultimately improving healthcare quality, patient outcomes, and decision-making processes.

According to the World Health Organization (WHO), "hearing impairment" is defined as "hearing loss greater than 40 decibels (dB) in the better ear in adults or greater than 30 dB in children." Therefore, "Persons with hearing impairments" refers to individuals who have a significant degree of hearing loss that affects their ability to communicate and perform daily activities.

Hearing impairment can range from mild to profound and can be categorized as sensorineural (inner ear or nerve damage), conductive (middle ear problems), or mixed (a combination of both). The severity and type of hearing impairment can impact the communication methods, assistive devices, or accommodations that a person may need.

It is important to note that "hearing impairment" and "deafness" are not interchangeable terms. While deafness typically refers to a profound degree of hearing loss that significantly impacts a person's ability to communicate using sound, hearing impairment can refer to any degree of hearing loss that affects a person's ability to hear and understand speech or other sounds.

Protein interaction mapping is a research approach used to identify and characterize the physical interactions between different proteins within a cell or organism. This process often involves the use of high-throughput experimental techniques, such as yeast two-hybrid screening, mass spectrometry-based approaches, or protein fragment complementation assays, to detect and quantify the binding affinities of protein pairs. The resulting data is then used to construct a protein interaction network, which can provide insights into functional relationships between proteins, help elucidate cellular pathways, and inform our understanding of biological processes in health and disease.

Functional laterality, in a medical context, refers to the preferential use or performance of one side of the body over the other for specific functions. This is often demonstrated in hand dominance, where an individual may be right-handed or left-handed, meaning they primarily use their right or left hand for tasks such as writing, eating, or throwing.

However, functional laterality can also apply to other bodily functions and structures, including the eyes (ocular dominance), ears (auditory dominance), or legs. It's important to note that functional laterality is not a strict binary concept; some individuals may exhibit mixed dominance or no strong preference for one side over the other.

In clinical settings, assessing functional laterality can be useful in diagnosing and treating various neurological conditions, such as stroke or traumatic brain injury, where understanding any resulting lateralized impairments can inform rehabilitation strategies.

Adverse Drug Reaction (ADR) Reporting Systems are spontaneous reporting systems used for monitoring the safety of authorized medicines in clinical practice. These systems collect and manage reports of suspected adverse drug reactions from healthcare professionals, patients, and pharmaceutical companies. The primary objective of ADR reporting systems is to identify new risks or previously unrecognized risks associated with the use of a medication, monitor the frequency and severity of known adverse effects, and contribute to post-marketing surveillance and pharmacovigilance activities.

Healthcare professionals, including physicians, pharmacists, and nurses, are encouraged to voluntarily report any suspected adverse drug reactions they encounter during their practice. In some countries, patients can also directly report any suspected adverse reactions they experience after taking a medication. Pharmaceutical companies are obligated to submit reports of adverse events identified through their own pharmacovigilance activities or from post-marketing surveillance studies.

The data collected through ADR reporting systems are analyzed to identify signals, which are defined as new, changing, or unknown safety concerns related to a medicine or vaccine. Signals are further investigated and evaluated for causality and clinical significance. If a signal is confirmed, regulatory actions may be taken, such as updating the product label, issuing safety communications, or restricting the use of the medication.

Examples of ADR reporting systems include the US Food and Drug Administration's (FDA) Adverse Event Reporting System (FAERS), the European Medicines Agency's (EMA) EudraVigilance, and the World Health Organization's (WHO) Uppsala Monitoring Centre.

In the context of medicine, "translating" often refers to the process of turning basic scientific discoveries into clinical applications that can directly benefit patients. This is also known as "translational research." It involves taking findings from laboratory studies and experiments, and finding ways to use that knowledge in the development of new diagnostic tools, treatments, or medical practices.

The goal of translation is to bridge the gap between scientific discovery and clinical practice, making sure that new advances in medicine are both safe and effective for patients. This process can be complex and challenging, as it requires collaboration between researchers, clinicians, regulatory agencies, and industry partners. It also involves rigorous testing and evaluation to ensure that any new treatments or interventions are both safe and effective.

Sensitivity and specificity are statistical measures used to describe the performance of a diagnostic test or screening tool in identifying true positive and true negative results.

* Sensitivity refers to the proportion of people who have a particular condition (true positives) who are correctly identified by the test. It is also known as the "true positive rate" or "recall." A highly sensitive test will identify most or all of the people with the condition, but may also produce more false positives.
* Specificity refers to the proportion of people who do not have a particular condition (true negatives) who are correctly identified by the test. It is also known as the "true negative rate." A highly specific test will identify most or all of the people without the condition, but may also produce more false negatives.

In medical testing, both sensitivity and specificity are important considerations when evaluating a diagnostic test. High sensitivity is desirable for screening tests that aim to identify as many cases of a condition as possible, while high specificity is desirable for confirmatory tests that aim to rule out the condition in people who do not have it.

It's worth noting that sensitivity and specificity are often influenced by factors such as the prevalence of the condition in the population being tested, the threshold used to define a positive result, and the reliability and validity of the test itself. Therefore, it's important to consider these factors when interpreting the results of a diagnostic test.

In the context of healthcare, an Information System (IS) is a set of components that work together to collect, process, store, and distribute health information. This can include hardware, software, data, people, and procedures that are used to create, process, and communicate information.

Healthcare IS support various functions within a healthcare organization, such as:

1. Clinical information systems: These systems support clinical workflows and decision-making by providing access to patient records, order entry, results reporting, and medication administration records.
2. Financial information systems: These systems manage financial transactions, including billing, claims processing, and revenue cycle management.
3. Administrative information systems: These systems support administrative functions, such as scheduling appointments, managing patient registration, and tracking patient flow.
4. Public health information systems: These systems collect, analyze, and disseminate public health data to support disease surveillance, outbreak investigation, and population health management.

Healthcare IS must comply with various regulations, including the Health Insurance Portability and Accountability Act (HIPAA), which governs the privacy and security of protected health information (PHI). Effective implementation and use of healthcare IS can improve patient care, reduce errors, and increase efficiency within healthcare organizations.

A database, in the context of medical informatics, is a structured set of data organized in a way that allows for efficient storage, retrieval, and analysis. Databases are used extensively in healthcare to store and manage various types of information, including patient records, clinical trials data, research findings, and genetic data.

As a topic, "Databases" in medicine can refer to the design, implementation, management, and use of these databases. It may also encompass issues related to data security, privacy, and interoperability between different healthcare systems and databases. Additionally, it can involve the development and application of database technologies for specific medical purposes, such as clinical decision support, outcomes research, and personalized medicine.

Overall, databases play a critical role in modern healthcare by enabling evidence-based practice, improving patient care, advancing medical research, and informing health policy decisions.

Computer-assisted radiographic image interpretation is the use of computer algorithms and software to assist and enhance the interpretation and analysis of medical images produced by radiography, such as X-rays, CT scans, and MRI scans. The computer-assisted system can help identify and highlight certain features or anomalies in the image, such as tumors, fractures, or other abnormalities, which may be difficult for the human eye to detect. This technology can improve the accuracy and speed of diagnosis, and may also reduce the risk of human error. It's important to note that the final interpretation and diagnosis is always made by a qualified healthcare professional, such as a radiologist, who takes into account the computer-assisted analysis in conjunction with their clinical expertise and knowledge.

"Evaluation studies" is a broad term that refers to the systematic assessment or examination of a program, project, policy, intervention, or product. The goal of an evaluation study is to determine its merits, worth, and value by measuring its effects, efficiency, and impact. There are different types of evaluation studies, including formative evaluations (conducted during the development or implementation of a program to provide feedback for improvement), summative evaluations (conducted at the end of a program to determine its overall effectiveness), process evaluations (focusing on how a program is implemented and delivered), outcome evaluations (assessing the short-term and intermediate effects of a program), and impact evaluations (measuring the long-term and broad consequences of a program).

In medical contexts, evaluation studies are often used to assess the safety, efficacy, and cost-effectiveness of new treatments, interventions, or technologies. These studies can help healthcare providers make informed decisions about patient care, guide policymakers in developing evidence-based policies, and promote accountability and transparency in healthcare systems. Examples of evaluation studies in medicine include randomized controlled trials (RCTs) that compare the outcomes of a new treatment to those of a standard or placebo treatment, observational studies that examine the real-world effectiveness and safety of interventions, and economic evaluations that assess the costs and benefits of different healthcare options.

Speech Therapy, also known as Speech-Language Pathology, is a medical field that focuses on the assessment, diagnosis, treatment, and prevention of communication and swallowing disorders in children and adults. These disorders may include speech sound production difficulties (articulation disorders or phonological processes disorders), language disorders (expressive and/or receptive language impairments), voice disorders, fluency disorders (stuttering), cognitive-communication disorders, and swallowing difficulties (dysphagia).

Speech therapists, who are also called speech-language pathologists (SLPs), work with clients to improve their communication abilities through various therapeutic techniques and exercises. They may also provide counseling and education to families and caregivers to help them support the client's communication development and management of the disorder.

Speech therapy services can be provided in a variety of settings, including hospitals, clinics, schools, private practices, and long-term care facilities. The specific goals and methods used in speech therapy will depend on the individual needs and abilities of each client.

Brain mapping is a broad term that refers to the techniques used to understand the structure and function of the brain. It involves creating maps of the various cognitive, emotional, and behavioral processes in the brain by correlating these processes with physical locations or activities within the nervous system. Brain mapping can be accomplished through a variety of methods, including functional magnetic resonance imaging (fMRI), positron emission tomography (PET) scans, electroencephalography (EEG), and others. These techniques allow researchers to observe which areas of the brain are active during different tasks or thoughts, helping to shed light on how the brain processes information and contributes to our experiences and behaviors. Brain mapping is an important area of research in neuroscience, with potential applications in the diagnosis and treatment of neurological and psychiatric disorders.

Speech perception is the process by which the brain interprets and understands spoken language. It involves recognizing and discriminating speech sounds (phonemes), organizing them into words, and attaching meaning to those words in order to comprehend spoken language. This process requires the integration of auditory information with prior knowledge and context. Factors such as hearing ability, cognitive function, and language experience can all impact speech perception.

Verbal learning is a type of learning that involves the acquisition, processing, and retrieval of information presented in a verbal or written form. It is often assessed through tasks such as list learning, where an individual is asked to remember a list of words or sentences after a single presentation or multiple repetitions. Verbal learning is an important aspect of cognitive functioning and is commonly evaluated in neuropsychological assessments to help identify any memory or learning impairments.

Pharmaceutical preparations refer to the various forms of medicines that are produced by pharmaceutical companies, which are intended for therapeutic or prophylactic use. These preparations consist of an active ingredient (the drug) combined with excipients (inactive ingredients) in a specific formulation and dosage form.

The active ingredient is the substance that has a therapeutic effect on the body, while the excipients are added to improve the stability, palatability, bioavailability, or administration of the drug. Examples of pharmaceutical preparations include tablets, capsules, solutions, suspensions, emulsions, ointments, creams, and injections.

The production of pharmaceutical preparations involves a series of steps that ensure the quality, safety, and efficacy of the final product. These steps include the selection and testing of raw materials, formulation development, manufacturing, packaging, labeling, and storage. Each step is governed by strict regulations and guidelines to ensure that the final product meets the required standards for use in medical practice.

Automation in the medical context refers to the use of technology and programming to allow machines or devices to operate with minimal human intervention. This can include various types of medical equipment, such as laboratory analyzers, imaging devices, and robotic surgical systems. Automation can help improve efficiency, accuracy, and safety in healthcare settings by reducing the potential for human error and allowing healthcare professionals to focus on higher-level tasks. It is important to note that while automation has many benefits, it is also essential to ensure that appropriate safeguards are in place to prevent accidents and maintain quality of care.

Medical Definition:

Magnetic Resonance Imaging (MRI) is a non-invasive diagnostic imaging technique that uses a strong magnetic field and radio waves to create detailed cross-sectional or three-dimensional images of the internal structures of the body. The patient lies within a large, cylindrical magnet, and the scanner detects changes in the direction of the magnetic field caused by protons in the body. These changes are then converted into detailed images that help medical professionals to diagnose and monitor various medical conditions, such as tumors, injuries, or diseases affecting the brain, spinal cord, heart, blood vessels, joints, and other internal organs. MRI does not use radiation like computed tomography (CT) scans.

Cerebral dominance is a concept in neuropsychology that refers to the specialization of one hemisphere of the brain over the other for certain cognitive functions. In most people, the left hemisphere is dominant for language functions such as speaking and understanding spoken or written language, while the right hemisphere is dominant for non-verbal functions such as spatial ability, face recognition, and artistic ability.

Cerebral dominance does not mean that the non-dominant hemisphere is incapable of performing the functions of the dominant hemisphere, but rather that it is less efficient or specialized in those areas. The concept of cerebral dominance has been used to explain individual differences in cognitive abilities and learning styles, as well as the laterality of brain damage and its effects on cognition and behavior.

It's important to note that cerebral dominance is a complex phenomenon that can vary between individuals and can be influenced by various factors such as genetics, environment, and experience. Additionally, recent research has challenged the strict lateralization of functions and suggested that there is more functional overlap and interaction between the two hemispheres than previously thought.

Natural-language programming Natural-language understanding Natural-language search Outline of natural language processing ... natural-language understanding, and natural-language generation. Natural language processing has its roots in the 1950s. ... 1960s: Some notably successful natural language processing systems developed in the 1960s were SHRDLU, a natural language ... Machine Learning of Natural Language. Springer-Verlag. ISBN 978-0-387-19557-5. Media related to Natural language processing at ...
... (QNLP) is the application of quantum computing to natural language processing (NLP). It ... Categorical quantum mechanics Natural language processing Quantum machine learning Applied category theory String diagram Zeng ... The first quantum algorithm for natural language processing used the DisCoCat framework and Grover's algorithm to show a ... It was later shown that quantum language processing is BQP-Complete, i.e. quantum language models are more expressive than ...
The history of natural language processing describes the advances of natural language processing (Outline of natural language ... Natural language processing, History of linguistics, History of software). ... Some notably successful NLP systems developed in the 1960s were SHRDLU, a natural language system working in restricted "blocks ... As a result, the Chomskyan paradigm discouraged the application of such models to language processing. McCorduck 2004, p. 286, ...
... natural-language understanding) Wolfram Language, and natural-language processing computation engine Wolfram Alpha. Victor ... Oxford English Corpus The following natural-language processing toolkits are notable collections of natural-language processing ... provider of a natural-language processing services. Wolfram Research, Inc. developer of natural-language processing computation ... Natural-language processing is used in programs designed to teach language, including first- and second-language training. ...
CS1 errors: missing periodical, CS1 German-language sources (de), Natural language processing, Semantics). ... Semantic decomposition is common in natural language processing applications. The basic idea of a semantic decomposition is ... as the main problem of language understanding. As an AI-complete environment, WSD is a core problem of natural language ... Given that an AI does not inherently have language, it is unable to think about the meanings behind the words of a language. An ...
... is the book series of the Association for Computational Linguistics, published by ... Professor of Language Engineering(Emeritus)in School of Computer Science (University of Manchester). (Natural language ... The editorial board has the following members: Chu-Ren Huang, Chair Professor of Applied Chinese Language Studies in the ... Professor of Information Processing and Internet in the Informatics Institute (the University of Amsterdam) and Harold Somers, ...
... (EMNLP) is a leading conference in the area of natural language processing and ... it is one of the two primary high impact conferences for natural language processing research. EMNLP is organized by the ACL ... Natural language processing). ...
For textual documents, the interpretation can use natural language processing (NLP) technologies. Document automation Document ... for example using natural language processing (NLP) or image classification technologies. It is applied in many industrial and ... Natural Language Processing (NLP) or Intelligent Character Recognition (ICE) to extract data from several types documents. ... Document processing is a field of research and a set of production processes aimed at making an analog document digital. ...
"Natural Language Processing". Research at Google. "Google Tricks #25 Tricks which google can Do for you and you never knew". ... In late 2011 Google added a graphical calculator to search results, using natural language processing to determine that search ... Users can set any of these languages (except pig Latin) as their search settings' preferred language. ( see it )When Ken Perlin ... a turkey language was added to the selection of languages to which translations could be made. An example translation provided ...
... natural language processing; language acquisition, and so forth. Mémoires de la Société de Linguistique de Paris (list of ... French-language journals, French-language literature, Publications established in 1869, 1869 establishments in France, All stub ... Articles containing French-language text, Annual journals (infobox), Linguistics journals, ...
2004 Founder and President of the Federation on Natural Language Processing 2001-2004 Natural language processing (VRQ), ... Natural Language Processing. Lecture Notes in Computer Science Volume 1835:1-15. Dordrecht: Springer. ISBN 978-3-540-67605-8 Di ... In 2004 she founded the Federation on Natural Language Processing, bringing together main actors in the area of theoretical ... Anna Maria Di Sciullo Laboratoire de recherche sur les asymétries d'interface Federation on Natural Language Processing ...
Natural Language Processing (NLP) The Natural Language Processing group focuses on the semantics and pragmatics of discourse. ... "Natural Language Processing". Retrieved 22 January 2017. "H-ITS , Physics of Stellar Objects". Archived from the original on ... The aim is to use the computer for understanding and generating language and texts and to make use of computers more naturally ... Situated at the intersection of the natural sciences, mathematics, and computer science, it is dedicated to the exploration of ...
The model attempted to provide developers and users with an advanced natural language processing tool that can effectively ... Washington found that GPT-3 produced toxic language at a toxicity level comparable to the similar natural language processing ... It works just as well for impossible languages as for actual languages. It is therefore refuted, if intended as a language ... One architecture used in natural language processing (NLP) is a neural network based on a deep learning model that was first ...
In natural language processing (NLP), a text graph is a graph representation of a text item (document, passage or sentence). It ... Gabor Melli's page on text graphs Description of text graphs from a semantic processing perspective. (Natural language ... is a series of regular academic workshops intended to encourage the synergy between the fields of natural language processing ( ... Graph-based methods for applications on social networks Rumor proliferation E-reputation Multiple identity detection Language ...
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See natural language processing.) From a technical point of view, semantic queries are precise relational-type operations much ... Dworetzky, Tom (2011). "How Siri Works: iPhone's 'Brain' Comes from Natural Language Processing". International Business Times ... Semantic queries are used in triplestores, graph databases, semantic wikis, natural language and artificial intelligence ... "SPARQL Query Language for RDF". W3C. 2008. Semantic queries in databases: problems and challenges. ACM Digital Library. 2009. ...
"Natural Language Processing". City University of New York. Retrieved 14 March 2017. "Natural Language Processing". Columbia ... "Natural Language Processing Group". University of Notre Dame. Retrieved 14 March 2017. "Natural Language Processing Group". ... "Natural Language Processing". IBM. 2016-07-25. Retrieved 14 March 2017. "Natural Language Processing (NLP) group". Idiap ... "Natural Language Processing". University of Illinois. Retrieved 14 March 2017. "Natural Language Processing & Portuguese- ...
Natural Language Processing Journal. 4: 100025. arXiv:2303.07201. doi:10.1016/j.nlp.2023.100025. ISSN 2949-7191. Menon, Bindu; ... Bureau, The Hindu (26 February 2023). "Sanskrit is a universal language: ISKCON's Bhakti Raghava Swami". The Hindu. ISSN 0971- ... Kannada and Sanskrit languages Rhythm of the Spirit by Sushrut Badhe, Cyberwit.net, 2014 Voice of Krishna: Secrets of the Self ... Krishna's Butter project launched online classrooms for children worldwide in six Indian regional languages. Sushrut started an ...
"Penn Natural Language Processing". nlp.cis.upenn.edu. Retrieved 15 March 2019. "The ACL Archives: ACL Officers". www.aclweb.org ... Natural language processing researchers, American women academics, 21st-century American women, Presidents of the Association ... Language Resources and Evaluation. 42 (1): 21-40. doi:10.1007/s10579-007-9048-2. S2CID 8071367. "History - The UT Austin ...
... is a natural language processing framework which draws on theoretical and descriptive linguistics. ... Natural Language Engineering, 6(1):99 - 108, 2000. Hans Uszkoreit. New Chances for Deep Linguistic Processing Archived 2005-11- ... Integrating Deep and Shallow Natural Language Processing Components - Representations and Hybrid Architectures. Ph.D. thesis, ... Combinatory categorial grammar Head-driven phrase structure grammar Lexical functional grammar Natural language processing Tree ...
Parsing existing Pop music (for content or word choice, e.g.) This involves natural language processing. Pablo Gervás has ... Creativity research in jazz has focused on the process of improvisation and the cognitive demands that this places on a musical ... Hillsdale, NJ: Lawrence Erlbaum Associates Turner, S.R. (1994), The Creative Process: A Computer Model of Storytelling, ... Case-based Approach to Figurative Language, Proceedings of AAAI 2007, the 22nd AAAI Conference on Artificial Intelligence. ...
Natural language parsing, Natural language processing, Natural language processing toolkits, Python (programming language) ... libraries, Statistical natural language processing, All stub articles, Programming language topic stubs). ... Bird, Steven; Klein, Ewan; Loper, Edward (2009). Natural Language Processing with Python. O'Reilly Media Inc. ISBN 978-0-596- ... The Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and programs for symbolic and statistical natural ...
Lexicography and Natural Language Processing. A Festschrift in Honour of B.T.S. Atkins. Lexicographica: International Annual ... Lexicography and Natural Language Processing. A Festschrift in Honour of B.T.S. Atkins. Lexicographica: International Annual ... Also in Challenges in Natural Language Processing, M. Bates and R. Weischedel (eds.), Cambridge University Press, Cambridge ( ... euralex.org British National Corpus website VIEW query interface for the BNC Lexicography and Natural Language Processing: A ...
"Project ParGram , Institute for Natural Language Processing , University of Stuttgart". www.ims.uni-stuttgart.de. Retrieved ... Linguists of Indo-Aryan languages, Linguists of Urdu, Linguists of Hindi, Linguists of German, Stanford University alumni, ...
Studies in Natural Language Processing. Cambridge: Cambridge University Press. p. 26. ISBN 978-0-521-66340-3. Heselwood 2013, ... Gbe languages, Manding languages, Lingala, etc. Capital case variants have been created for use in these languages. For example ... but to make it usable for other languages the values of the symbols were allowed to vary from language to language. For example ... found in the Khoisan languages and some neighboring Bantu languages of Africa), implosives (found in languages such as Sindhi, ...
Association for Natural Language Processing: "A Study on Constants of Natural Language Texts" "Kumiko Tanaka-ISHII RCAST". ... "Study on Constants of Natural Language Texts". Journal of Natural Language Processing. 21 (4): 877-895. doi:10.5715/jnlp.21.877 ... Kageura, Kyo (6 January 2013). "Reflecting on Human Language Through Computer Languages". Semiotica. 2013 (195). doi:10.1515/ ... Personal Website Kumiko Tanaka-Ishii Group Website at the University of Tokyo (CS1 French-language sources (fr), CS1 maint: ...
... : A Language-Independent System for Parsing Unrestricted Text. Natural Language Processing, No 4. Mouton de ... ANLC '94 Proceedings of the fourth conference on Applied natural language processing. CG Tutorial by Kevin Donnelly VISL CG-3, ... Constraint grammar (CG) is a methodological paradigm for natural language processing (NLP). Linguist-written, context-dependent ... CG methodology has also been used in a number of language technology applications, such as spell checkers and machine ...
Tomabechi, Hideto (1995). "Design of Efficient Unification for Natural Language". Journal of Natural Language Processing. 2 (2 ... 英人, 苫米地 (15 July 1992). "Head-driven natural language processing by massively parallel constraint propagation". システム/制御/情報: ... Hideto Tomabechi, JustSystem papers". 自然言語処理 = Journal of Natural Language Processing. 2 (2): 23-58. "Construction of a ... He has published papers on LISP, P2P, natural language processing, computer science, neural networks, functional brain science ...
In natural language processing and information retrieval, cluster labeling is the problem of picking descriptive, human- ... Stanford Natural Language Processing Group. Web. 25 Nov. 2009. . Manning, Christopher D., Prabhakar Raghavan, and Hinrich ... Stanford Natural Language Processing Group. Web. 25 Nov. 2009. . Manning, Christopher D., Prabhakar Raghavan, and Hinrich ... Stanford Natural Language Processing Group. Web. 25 Nov. 2009. . Francois Role, Moahmed Nadif. Beyond cluster labeling: ...
Indurkhya, N., Damerau, F. (2010). Handbook of Natural Language Processing. Chapman & Hall/CRC. p. 594.{{cite book}}: CS1 maint ... Natural language processing researchers, Presidents of the Association for Computational Linguistics). ... Hearst, Marti A. (2011-11-01). "'Natural' Search User Interfaces". Communications of the ACM, Vol. 54, No. 11. Association for ... Edge Foundation contributing author and a member of the Usage panel of the American Heritage Dictionary of the English Language ...
The Applied Natural Language Processing track is a forum for researchers working in natural language processing (NLP), ... Applied Natural Language Processing, FLAIRS, p., 2008.. Philip M. McCarthy ,,Scott A. Crossley. Applied Natural Language ... Crossley Applied Natural Language Processing FLAIRS 2008, .. Philip M. McCarthy ,,Scott A. Crossley (2008). Applied Natural ... In addition, natural language processing can facilitate human-computer interaction for people with special needs, assist in the ...
Natural Language Processing for Computational Social Science. Invited Tutorial at NIPS. *Percy Liang and Dan Klein. 2007. ... Learning to detect hedges and their scope in natural language text. Fourteenth Conference on Computational Natural Language ... 2016 Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health TACL. ... In the Workshop on Natural Language Processing and Computational Social Science, 53--62. ...
Natural language processing (NLP) is a branch of artificial intelligence (AI) that enables computers to comprehend, generate, ... What Is Natural Language Processing (NLP)?. Natural Language Processing (NLP) Defined. Natural language processing (NLP) is a ... Natural language processing has the ability to interrogate the data with natural language text or voice. This is also called " ... Applications of Natural Language Processing. Automate routine tasks: Chatbots powered by NLP can process a large number of ...
Natural Language Processing with Java. Java for Data Science Getting Started with Natural Language Processing in Java ... Natural Language Processing (NLP) is a broad topic focused on the use of computers to analyze natural languages. It addresses ... Natural Language Generation: This is the process of generating text from a data or knowledge source, such as a database. It can ... There are several factors that makes this process hard. For example, there are hundreds of natural languages, each of which has ...
Short for natural language processing, NLP is a branch of artificial intelligence that deals with analyzing, understanding and ... Challenges of Natural Language Processing. One of the challenges inherent in natural language processing is teaching computers ... to interface with computers in both written and spoken contexts using natural human languages instead of computer languages. ... understanding and generating the languages that humans use naturally in order ...
... of ~20.8%, See Why - published on openPR.com ... Scope of the Report of Natural Language Processing. Natural language processing (NLP) is a branch of AI, Which helps computers ... Opportunities of the Natural Language Processing. Chapter 4: Presenting the Natural Language Processing Market Factor Analysis ... Global Natural Shampoo Market 2017 : Jason Natural, Avalon Natural Products, Rev … Global Natural Shampoo Market 2016 - 2017 A ...
... has steadily been increasing its presence in the natural language processing (NLP) community. With a few new hires this fall ... and a big performance at this years Conference on Empirical Methods in Natural Language Processing (EMNLP), ML@GT is becoming ... Georgia Tech researchers are tackling problems like how to summarize text and interpreting how persuasive language impacts ... https://mlatgt.blog/2020/11/16/mlgt-further-establishes-itself-in-natural-langu… ...
This series explores core concepts of natural language processing, starting with an introduction to the field and explaining ... Introduction to Natural Language Processing. Natural language processing is a set of techniques that allows computers and ... Vision Transformers: Natural Language Processing (NLP) Increases Efficiency…. *Natural Language Processing Pipelines, Explained ... Introduction to Natural Language Processing, Part 1: Lexical Units. This series explores core concepts of natural language ...
Digital Signal Processing, Natural Language Processing, Machine Learning, Image Processing and Computer Vision. ... Word Vectors in Natural Language Processing: Global Vectors (GloVe). A well-known model that learns vectors or words from their ... Get the FREE ebook The Great Big Natural Language Processing Primer and The Complete Collection of Data Science Cheat Sheets ... Get the FREE ebook The Great Big Natural Language Processing Primer and The Complete Collection of Data Science Cheat Sheets ...
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... strategies to automate the process for cancer registrars review this text to identify reportable cancer cases and enter data ... Natural language processing: The technology used to help computers understand human language. In this case, it refers to the ... CDC is using natural language processing (NLP) strategies to automate this process. ... Español , Other Languages Centers for Disease Control and Prevention. CDC twenty four seven. Saving Lives, Protecting People ...
The Department focuses on research, technological development and innovation in the field of natural language processing as ... English and other natural languages. Furthermore, the Department includes methods of creating language resources with manual ... natural language production, automatic translation and a multitude of applications of these technologies in digital content in ... Emphasis is put on processing monolingual and multilingual text data and content and their interaction with data and content ...
Supporting the Capture of Social Needs Through Natural Language Processing. Lewis J. Frey, Chanita Hughes Halbert, Christopher ... Supporting the Capture of Social Needs Through Natural Language Processing. Lewis J. Frey, Chanita Hughes Halbert, Christopher ... Supporting the Capture of Social Needs Through Natural Language Processing Message Subject (Your Name) has sent you a message ... Supporting the Capture of Social Needs Through Natural Language Processing. Lewis J. Frey, Chanita Hughes Halbert and ...
Natural language processing (NLP) is a type of artificial intelligence that focuses on the ability of a computer to understand ... GPT-3 is a powerful tool for processing and generating text and has helped to advance the field of natural language processing ... We are Not Ready for The Next Leap in AI, Natural Language Processing Subscribe Share. ... We are Not Ready for The Next Leap in AI, Natural Language Processing. * By Tom Romanoff , ...
... and challenges of applied natural language processing (NLP) and large language models (LLMs). ... John Snow Labs Announces Finance NLP and Legal NLP, Bringing State-of-the-Art Natural Language Processing to New Domains. ... John Snow Labs Announces Program for the 2023 NLP Summit, the Worlds Largest Gathering on Applied Natural Language Processing ... We open up language to everyone.. Languages: 150 languages and 40 areas of expertise.. Translated Rome, Italy. +390690254001. ...
Language Processing during Natural Sleep in a 6-Year-Old Boy, as Assessed with Functional MR Imaging. Bruce Nolan ... Language Processing during Natural Sleep in a 6-Year-Old Boy, as Assessed with Functional MR Imaging ... Language Processing during Natural Sleep in a 6-Year-Old Boy, as Assessed with Functional MR Imaging ... Language Processing during Natural Sleep in a 6-Year-Old Boy, as Assessed with Functional MR Imaging ...
This guide for Natural Language Processing is used in a variety of applications, like in a chatbot that responds to your ... What is Natural Language Processing (NLP)?. Natural Language Processing (NLP) is a branch of artificial intelligence that ... Welcome to the transformative world of Natural Language Processing (NLP). Here, the elegance of human language meets the ... Natural language processing (NLP) has undergone a revolution thanks to transfer learning, which enables models to use prior ...
This is particularly the case of knowledge-based approaches to natural language processing as near-human symbolic understanding ... Bridging the Statistic-Symbolic Representational Gap in Natural Language Processing. Submitted by José Manuel Góm... on 02/28/ ... Bridging the Statistic-Symbolic Representational Gap in Natural Language Processing ...
Challenges in natural language processing frequently involve speech recognition, natural-language understanding, and natural- ... The field of study that focuses on the interactions between human language and computers is called natural language processing ... Although natural language processing continues to evolve, there are already many ways in which it is being used today. Most of ... Natural Language Processing (NLP) is an incredible technology that allows computers to understand and respond to written and ...
Embeddings in Natural Language Processing: Theory and Advances in Vector Representations of Meaning In Special Collection: ... Embeddings in Natural Language Processing: Theory and Advances in Vector Representations of Meaning ... Marcos Garcia; Embeddings in Natural Language Processing: Theory and Advances in Vector Representations of Meaning. ... They have been used to represent the meaning of various units of natural languages, including, among others, words, phrases, ...
The data processing system nests language information for nested application programs. A fall-back mechanism is used to provide ... The system may send and receive messages in multiple natural languages for multiple users using different application programs ... a default language when a language desired by a user is not available or when nested languages are not available. ... wherein a link for an application program stores a natural language and a pointer to its message file, wherein the operating ...
DaNLP: An open-source toolkit for Danish Natural Language Processing. Pauli, A. B., Barrett, M. J., Lacroix, O. & Hvingelby, R. ... Saitov, K. & Derczynski, L., 20 Apr 2021, Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing. ... Experimental Standards for Deep Learning in Natural Language Processing Research. Ulmer, D. T., Bassignana, E., Müller- ... Findings of 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP). 20 p.. Research output: Conference ...
Natural language generation, Sentiment Analysis, Sentence Segmentation techniques. ... Find our latest blog on 7 best Natural Language Techniques (NLP) to extract information from any text/corpus document. An in- ... Natural language processing (NLP), as the title clears our perception that it has a sort of processing to do with language or ... 5. Natural Language Generation. Natural language generation (NLG) is a technique that uses raw structured data to convert it ...
Natural Language Processing. *Transformers. Audio. *Audio Embeddings. *Sound Classification. *Pitch Estimation. *Speech to Text ... Semantic segmentation describes the process of associating each pixel of an image with a class label, (such as flower, person, ... All inputs are RGB images, outputs are heatmaps and part affinity fields (PAFs) which via post processing perform pose ... They are useful in digitizating audio files for downstream text processing tasks such as text summarization and sentiment ...
Natural Language Processing Natural Language Processing research aims to design algorithms and methods that enable computers to ... Natural Language Processing research at UMD is highly interdisciplinary, and builds on the intellectual diversity of the ... natural language processing, and neural computation. The AI group has consistently ranked high in external national assessments ... Natural Language Processing, Systems, Algorithms, and others. The University of Maryland Computer Science Department, and other ...
Impacter Pathway brings Natural Language Processing to the educational realm through a technique called "Word Embedding" where ...
NLP could be a big boon in conversation building regional language chatbots but a lot needs to be done before we get there. ... Natural Language Processing: A Big Leap in Conversing with the Human Mind. NLP could be a big boon in conversation building ... The major field that gives voice assistants this power is Natural Language Processing or (NLP). Simply put, NLP makes it ... Natural Language Processing: A Big Leap in Conversing with the Human Mind ...
  • If you're interested in using some of these techniques with Python, take a look at the Jupyter Notebook about Python's natural language toolkit (NLTK) that I created. (teaco.nl)
  • In Python, there are stop-word lists for different languages in the nltk module itself, somewhat larger sets of stop words are provided in a special stop-words module - for completeness, different stop-word lists can be combined. (elalameya-group.com)
  • Topping our list is Natural Language Toolkit (NLTK), which is widely considered the best Python library for NLP. (kemeisc.com)
  • NLTK supports various languages, as well as named entities for multi language. (kemeisc.com)
  • Because NLTK is a string processing library, it takes strings as input and returns strings or lists of strings as output. (kemeisc.com)
  • Libraries like NLTK, SpaCy, and TensorFlow make it easier to build a chatbot that not only understands human language but also learns from user interactions. (hubspot.com)
  • Starting in the late 1980s, however, there was a revolution in natural language processing with the introduction of machine learning algorithms for language processing. (wikipedia.org)
  • The Workbench was developed as a platform for members of the health care community to develop and share NLP pipelines, language models, and other algorithms that convert unstructured clinical text to coded data. (cdc.gov)
  • As a multidisciplinary field, natural language processing (NLP) combines insights from linguistics, computer science, and machine learning to create algorithms that can understand textual data, making it a cornerstone of today's AI applications. (analyticsvidhya.com)
  • NLP uses rule-based and machine learning algorithms for various applications, such as text classification, extraction, machine translation, and natural language generation. (teaco.nl)
  • Up to the 1980s, the evolution originated in natural language processing with the introduction of Machine Learning algorithms for language processing. (analyticssteps.com)
  • We should translate the human language logically if we want the computer algorithms to interpret these data. (onpassive.com)
  • These algorithms process the input data to identify patterns and relationships between words, phrases and sentences and then use this information to determine the meaning of the text. (sandiego.edu)
  • NLP involves applying algorithms to recognize and extricate the natural language laws with the end goal that the unstructured language data is changed over into a structure that Computers can comprehend. (digitalvisi.com)
  • This involves using natural language processing algorithms to analyze unstructured data and automatically produce content based on that data. (vikramstudio46.com)
  • Document understanding algorithms analyze the content of documents with an encoder-decoder pipeline that combines computer vision (CV) and natural language processing (NLP) methods. (acgaudyt.pl)
  • Gensim relies on algorithms to process input larger than RAM. (kemeisc.com)
  • Chatbots powered by NLP can process a large number of routine tasks that are handled by human agents today, freeing up employees to work on more challenging and interesting tasks. (oracle.com)
  • Various applications use this Natural Language Processing guide, such as chatbots responding to your questions, search engines tailoring results based on semantics, and voice assistants setting reminders for you. (analyticsvidhya.com)
  • Natural language processing for chatbots gives them a human-like appearance. (onpassive.com)
  • Social media companies are actively using sentiment analysis to identify and curb bad behavior/bad speech on their platforms, Google Translate can translate between 100s of languages, and chatbots in live customer service chat software are on the rise. (exxactcorp.com)
  • Three tools used commonly for natural language processing include Natural Language Toolkit , Gensim and Intel natural language processing Architect. (vikramstudio46.com)
  • The Applied Natural Language Processing track is a forum for researchers working in natural language processing (NLP), computational linguistics (CL), applied linguistics (AL) and related areas. (aaai.org)
  • Natural language processing (NLP) is a branch of artificial intelligence (AI) that enables computers to comprehend, generate, and manipulate human language. (oracle.com)
  • Short for n atural l anguage p rocessing , NLP is a branch of artificial intelligence that deals with analyzing, understanding and generating the languages that humans use naturally in order to interface with computers in both written and spoken contexts using natural human languages instead of computer languages. (webopedia.com)
  • In its fourth year, the event is the world's largest gathering of the artificial intelligence (AI) community that explores today's cutting edge use cases, advancements, and challenges of applied natural language processing (NLP) and large language models (LLMs). (multilingual.com)
  • Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on teaching machines to understand, interpret, and respond to human language. (analyticsvidhya.com)
  • Behind all these, there is a major Artificial Intelligence (AI) application called Natural Language Processing or NLP . (indiatimes.com)
  • A chatbot based on natural language processing (NLP) is a computer program or artificial intelligence that communicates with a consumer through text or sound. (onpassive.com)
  • This course introduces key concepts and methods in Natural Language Processing (NLP), the subfield of data science and artificial intelligence that deals with computer interaction with human language. (arrow.com)
  • Natural language processing is a branch of artificial intelligence that focuses on giving computers the ability to understand human language. (sandiego.edu)
  • Natural Language Processing Service , typically abbreviated as NLP, is a part of artificial intelligence that manages the cooperation among PCs and people utilizing the natural language. (digitalvisi.com)
  • Natural language processing Examples is a form of artificial intelligence (AI) that allows computers to understand human language, whether it be written, spoken, or even scribbled. (kelaax.com)
  • NLP is a branch of artificial intelligence that allows computers to understand and interact with human language, whether written or spoken. (kelaax.com)
  • Python is widely considered the best programming language, and it is critical for artificial intelligence (AI) and machine learning tasks. (kemeisc.com)
  • Python is an extremely efficient programming language when compared to other mainstream languages, and it is a great choice for beginners thanks to its English-like commands and syntax. (kemeisc.com)
  • Another one of the best aspects of the Python programming language is that it consists of a huge amount of open-source libraries, which make it useful for a wide range of tasks. (kemeisc.com)
  • There are many aspects that make Python a great programming language for NLP projects, including its simple syntax and transparent semantics. (kemeisc.com)
  • The Python library is often used to build natural language understanding systems and information extraction systems. (kemeisc.com)
  • With a range of powerful libraries for Natural Language Processing (NLP) and Machine Learning, even a beginner in Python can craft a functional chatbot. (hubspot.com)
  • Python is rich in libraries, especially when it comes to Natural Language Processing (NLP) and Machine Learning. (hubspot.com)
  • One of the challenges inherent in natural language processing is teaching computers to understand the way humans learn and use language. (webopedia.com)
  • This technology connects humans and computers, allowing for more natural interactions. (analyticsvidhya.com)
  • Let's be clear, computers are nowhere near the same intuitive understanding of natural language as humans. (vikramstudio46.com)
  • NLP can be useful in communicating with humans in their own language. (acgaudyt.pl)
  • By enabling computers to understand human language, interacting with computers becomes much more intuitive for humans. (elalameya-group.com)
  • NLP enables various applications and domains, such as healthcare, machine translation, e-commerce, etc., to interact with humans in a natural and intuitive way. (kelaax.com)
  • CDC is using natural language processing (NLP) strategies to automate this process. (cdc.gov)
  • We need to automate this type of process in order to extract the essence of the global data collected and learn its value. (sloboda-studio.com)
  • It is feasible to fully automate operations such as preparing financial reports or analyzing statistics using natural language understanding (NLU) and natural language generation (NLG). (onpassive.com)
  • This is a method that allows machines to create (natural language generation) and analyze (natural language understanding) the human language. (sloboda-studio.com)
  • With a vast amount of unstructured data being generated on a daily basis, it is increasingly difficult for organizations to process and analyze this information effectively. (sandiego.edu)
  • One example of this is in language models such as GPT3, which are able to analyze an unstructured text and then generate believable articles based on the text. (vikramstudio46.com)
  • These search engines use natural language understanding (NLU) to analyze the query and natural language generation (NLG) to produce the results. (kelaax.com)
  • Natural language processing (NLP) systems analyze and/or generate human language, typically on users' behalf. (aclanthology.org)
  • These include the analysis of online materials, most of them in textual form or text combined with other media (visual, audio), the use of innovative human-computer interfaces, such as interactive agents, which benefit from language understanding, and the use of computational tools to facilitate intelligent tutoring systems and instructional methodology. (aaai.org)
  • In addition, natural language processing can facilitate human-computer interaction for people with special needs, assist in the organization of classification systems, and coordinate text segmentation. (aaai.org)
  • NLP applies both to written text and speech, and can be applied to all human languages. (oracle.com)
  • Natural language understanding (NLU) and natural language generation (NLG) refer to using computers to understand and produce human language, respectively. (oracle.com)
  • The understanding by computers of the structure and meaning of all human languages, allowing developers and users to interact with computers using natural sentences and communication. (oracle.com)
  • Computational linguistics (CL) is the scientific field that studies computational aspects of human language, while NLP is the engineering discipline concerned with building computational artifacts that understand, generate, or manipulate human language. (oracle.com)
  • In the course of human communication, the meaning of the sentence depends on both the context in which it was communicated and each person's understanding of the ambiguity in human languages. (webopedia.com)
  • Natural language processing (NLP) is a branch of AI, Which helps computers understand, interpret and manipulate human language. (openpr.com)
  • Natural language processing draws from many disciplines, including computational linguistics and computer science, in its pursuit to fill the gap between computer understanding and human communication. (openpr.com)
  • The technology used to help computers understand human language. (cdc.gov)
  • Here, the elegance of human language meets the precision of machine intelligence. (analyticsvidhya.com)
  • This is particularly the case of knowledge-based approaches to natural language processing as near-human symbolic understanding relies on expressive, structured knowledge representations. (semantic-web-journal.net)
  • Natural Language Understanding (NLU) helps the machine to understand and analyse human language by extracting metadialog.com the metadata from content such as concepts, entities, keywords, emotion, relations, and semantic roles. (teaco.nl)
  • NLP primarily comprises two major functionalities, The first is " Human to Machine Translation " (Natural Language Understanding), and the second is " Machine to Human translation "(Natural Language Generation). (analyticssteps.com)
  • In the case of NLP, there is an intermediate process called conversion of human speech into computable properties or characteristics called feature vectors like intent, timing and sentiment. (indiatimes.com)
  • NLP assists your chatbot in analyzing and producing text from human language. (onpassive.com)
  • However, despite its structure, human language is chaotic. (onpassive.com)
  • IBM defines NLP as a field of study that seeks to build machines that can understand and respond to human language, mimicking the natural processes of human communication. (sandiego.edu)
  • A definitive goal of NLP is to peruse, translate, comprehend, and sort out human languages in a meaningful way. (digitalvisi.com)
  • Most NLP strategies depend on AI to get significance from human languages. (digitalvisi.com)
  • It's the idea of the human language that makes NLP disturbing. (digitalvisi.com)
  • Thoroughly understanding the human language requires understanding both the words and how the ideas are associated with conveying the planned message. (digitalvisi.com)
  • ChatGPT is capable of generating human-like responses to a wide variety of prompts, making it well-suited for a number of natural language processing tasks such as dialogue generation, question answering, and text completion. (pratapsharma.com.np)
  • ChatGPT is able to generate responses to a wide variety of prompts that are similar to those a human would provide, making it well-suited for natural language processing tasks such as dialogue generation, question answering, and text completion. (pratapsharma.com.np)
  • With ChatGPT, users can simply speak or type as they would with a human, which makes the process much more natural and intuitive. (pratapsharma.com.np)
  • In conclusion, ChatGPT is a revolutionary conversational language model that is capable of generating human-like responses to various prompts, making it suitable for natural language processing tasks. (pratapsharma.com.np)
  • However, I will try to show that "pivoting" the product, starting asking the right questions, and leveraging human natural abilities might be a game changer! (essex.ac.uk)
  • To understand human language is to understand not only the words, but the concepts and how they'relinked together to create meaning. (acgaudyt.pl)
  • Despite language being one of the easiest things for the human mind to learn, the ambiguity of language is what makes natural language processing a difficult problem for computers to master. (acgaudyt.pl)
  • Natural language processing is a field of research that provides us with practical ways of building systems that understand human language. (acgaudyt.pl)
  • Together, these technologies enable computers to process human language in the form of text or voice data and to 'understand' its full meaning, complete with the speaker or writer's intent and sentiment. (elalameya-group.com)
  • Natural language processing , or NLP, is a field of AI that aims to understand the semantics and connotations of natural human languages. (kemeisc.com)
  • Stanford CoreNLP is a library consisting of a variety of human language technology tools that help with the application of linguistic analysis tools to a piece of text. (kemeisc.com)
  • Or what if computers could understand a human language so well that it can estimate a probability telling you how likely it is to encounter any random sentence that you give it? (exxactcorp.com)
  • The premise of symbolic NLP is well-summarized by John Searle's Chinese room experiment: Given a collection of rules (e.g., a Chinese phrasebook, with questions and matching answers), the computer emulates natural language understanding (or other NLP tasks) by applying those rules to the data it confronts. (wikipedia.org)
  • CNNs turned out to be the natural choice given their effectiveness in computer vision tasks (Krizhevsky et al. (teaco.nl)
  • You can use its NLP APIs for language detection, text segmentation, named entity recognition, tokenization, and many other tasks. (acgaudyt.pl)
  • Aspect mining is often combined with sentiment analysis tools, another type of natural language processing to get explicit or implicit sentiments about aspects in text. (elalameya-group.com)
  • It converts a large set of text into more formal representations such as first-order logic structures that are easier for the computer programs to manipulate notations of the natural language processing. (teaco.nl)
  • The system interface is a set of commands available for use in application programs to configure and manipulate a data processing system. (justia.com)
  • The most direct way to manipulate a computer is through code - the computer's language. (elalameya-group.com)
  • The rapid pace of development in natural language processing in textual studies, speech recognition, speech production, and data mining has led to a revived interest in tools able to understand, organize and extract information from natural language sources. (aaai.org)
  • Challenges in natural language processing frequently involve speech recognition, natural-language understanding, and natural-language generation. (wikipedia.org)
  • Natural language processing is a set of techniques that allows computers and people to interact. (kdnuggets.com)
  • Simply put, NLP makes it possible for devices to process natural languages in written or spoken format and interact with us. (indiatimes.com)
  • Deep learning is a kind of machine learning that can learn very complex patterns from large datasets, which means that it is ideally suited to learning the complexities of natural language from datasets sourced from the web. (oracle.com)
  • It involves processing natural language datasets, such as text corpora or speech corpora, using either rule-based or probabilistic (i.e. statistical and, most recently, neural network-based) machine learning approaches. (wikipedia.org)
  • The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them. (wikipedia.org)
  • Lesk algorithm), reference (e.g., within Centering Theory) and other areas of natural language understanding (e.g., in the Rhetorical Structure Theory). (wikipedia.org)
  • For the alert investigator, fMRI can help to open some of these vistas and to improve our understanding of these processes. (ajnr.org)
  • Current approaches to natural language processing are based on deep learning, a type of AI that examines and uses patterns in data to improve a program's understanding. (elalameya-group.com)
  • This is the brain behind the chatbot's language understanding capabilities. (hubspot.com)
  • Understanding the applications and uses of natural language processing and other AI-enabled technologies could be the difference between success and failure for modern retailers. (defined.ai)
  • There are various programming languages and libraries available for NLP, each with its own strengths and weaknesses. (sandiego.edu)
  • Language data may be formal and textual, such as newspaper articles, or informal and auditory, such as a recording of a telephone conversation. (kdnuggets.com)
  • Processing huge textual data is a task that is impossible to perform manually. (sloboda-studio.com)
  • Learn about the shift from static word representations, like Word2Vec, to dynamic contextual embeddings, emphasizing how important context is for language comprehension. (analyticsvidhya.com)
  • The journey towards capturing the particulars of language can be seen in the change from conventional Bag-of-Words (BoW) models to Word2Vec and then to contextual embeddings. (analyticsvidhya.com)
  • The process of mapping sentences from character to strings and strings into words are initially the basic steps of any NLP problem because to understand any text or document we need to understand the meaning of the text by interpreting words/sentences present in the text. (analyticssteps.com)
  • Natural language processing is the application of the steps above - defining representations of information, parsing that information from the data generating process, and constructing, storing, and using data structures that store information - to information embedded in natural languages. (kdnuggets.com)
  • They also keep track of the most recent advances of this vibrant and fast-evolving area of research, discussing cross-lingual representations and current language models based on the Transformer. (mit.edu)
  • More critically, the principles that lead a deep language models to generate brain-like representations remain largely unknown. (vikramstudio46.com)
  • The present position is tied to the research project 'Interpreting and Grounding Pre-trained Representations for Natural Language Processing', a collaboration between Linköping University, Chalmers University of Technology, and Recorded Future AB. (lu.se)
  • This includes a wide range of activities: multilevel linguistic content analysis, information retrieval and extraction, natural language production, automatic translation and a multitude of applications of these technologies in digital content in Greek, English and other natural languages. (ilsp.gr)
  • We can see how a chatbot that uses natural language processing works: because the machine lacks linguistic experience, NLP entails educating the computer to interpret speech despite distractions. (onpassive.com)
  • Language interpretation applications like Google Decipher, for example, Word Processors, Microsoft Word, and Grammarly utilize NLP to check the linguistic exactness of writings. (digitalvisi.com)
  • In NLP, a syntactic investigation is utilized to survey how the natural language lines up with the linguistic standards. (digitalvisi.com)
  • Natural language processing has the ability to interrogate the data with natural language text or voice. (oracle.com)
  • Chapter 8 & 9: Displaying the Appendix, Methodology and Data Source finally, Natural Language Processing Market is a valuable source of guidance for individuals and companies. (openpr.com)
  • This series explores core concepts of natural language processing, starting with an introduction to the field and explaining how to identify lexical units as a part of data preprocessing. (kdnuggets.com)
  • Consider the process of extracting information from some data generating process: A company wants to predict user traffic on its website so it can provide enough compute resources (server hardware) to service demand. (kdnuggets.com)
  • Because they control the data generating process, they can add logic to the website that stores every request for data as a variable. (kdnuggets.com)
  • Natural language processing involves identifying and exploiting these rules with code to translate unstructured language data into information with a schema. (kdnuggets.com)
  • Language expressions from different contexts and data sources will have varying rules of grammar, syntax, and semantics. (kdnuggets.com)
  • Both approaches address challenges that laboratories and registries face when collecting, processing, and reporting cancer data. (cdc.gov)
  • Emphasis is put on processing monolingual and multilingual text data and content and their interaction with data and content reached by other means and modalities. (ilsp.gr)
  • The data processing system nests language information for nested application programs. (justia.com)
  • The present invention relates generally to data processing systems and more particularly to data processing systems which may communicate with users in multiple natural languages. (justia.com)
  • Most data processing and computer systems provide interaction with a user in only one natural language, such as English, French or German. (justia.com)
  • In most data processing systems, interaction with a user involves sending and receiving messages between the user and the data processing system. (justia.com)
  • To facilitate such message sending and receiving, a data processing system normally has standard instructions for sending output messages to the user and also has similar instructions for interpreting input messages received from the user. (justia.com)
  • An application programmer uses these instructions in an application program in order to provide interaction between a user and the data processing system when the program is run, i.e., when the system is configured by the program and processes described by the program are executed by the configured system. (justia.com)
  • It is a promising but dangerous IT field - we have learned how to collect and store terabytes of data, but still barely understand how to process it. (sloboda-studio.com)
  • As a report by EMC says, less than 1% of the world's data is analyzed and processed . (sloboda-studio.com)
  • Today we will explore the specifics of the best methods of data processing and compare the benefits of natural language processing and text mining. (sloboda-studio.com)
  • All we have to do is enter the data in our language, and the device will respond understandably. (onpassive.com)
  • Natural language processing (NLP) presents a solution to this problem, offering a powerful tool for managing unstructured data. (sandiego.edu)
  • Processing of the content's data. (digitalvisi.com)
  • The standards that direct the death of data utilizing natural languages are difficult for PCs to comprehend. (digitalvisi.com)
  • While a number of previous works exist that discuss ethical issues, in particular around big data and machine learning, to the authors' knowledge this is the first account of NLP and ethics from the perspective of a principled process. (aclanthology.org)
  • Please read the policy and regulations that guide how Lund University processes personal data in this context. (lu.se)
  • I am aware of the regulations and policies that guide how Lund University processes the personal data I voluntarily submit here. (lu.se)
  • Agent Based Modeling (ABM)), Natural Language Processing (NLP) and Big Data into entrepreneurship research. (lu.se)
  • Projects requiring natural language processing are generally organized by these sorts of challenges. (kdnuggets.com)
  • Tokenization is an integral part of any Information Retrieval(IR) system, it not only involves the pre-process of text but also generates tokens respectively that are used in the indexing/ranking process. (analyticssteps.com)
  • One of the other major benefits of spaCy is that it supports tokenization for more than 49 languages thanks to it being loaded with pre-trained statistical models and word vectors. (kemeisc.com)
  • We also encourage papers in information retrieval, speech processing and machine learning that present actual applications that can benefit from or have an impact on NLP/CL. (aaai.org)
  • Build, test, and deploy applications by applying natural language processing-for free. (oracle.com)
  • Information that is representational of natural language can also be useful for building powerful applications, such as bots that respond to questions or software that translates from one language to another. (kdnuggets.com)
  • Programming is not the focus, but you will do a bit of programming to actively experience the computational point of view on the world, creating applications in AI and robotics using friendly visual programming languages. (sfu.ca)
  • Natural language processing tools and techniques provide the foundation for implementing this technology in real-world applications. (sandiego.edu)
  • The Games4NLP workshop aims to promote and explore the possibilities for research and practical applications of using games and gamification for the creation of language resources for Natural Language Processing. (essex.ac.uk)
  • Natural language processing has a wide range of applications in business. (acgaudyt.pl)
  • Language translation is one of the most popular and useful applications of NLP. (kelaax.com)
  • SpaCy enables developers to create applications that can process and understand huge volumes of text. (kemeisc.com)
  • He has worked on NLP topics such as PoS-tagging, dependency parsing, and lexical semantics, and has developed resources and tools for different languages in both industry and academia. (mit.edu)
  • Before the deep learning tsunami , count-based vector space models had been successfully used in computational linguistics to represent the semantics of natural languages. (mit.edu)
  • For those who may not be familiar, ChatGPT by OpenAI is a conversational language model that was developed by OpenAI. (pratapsharma.com.np)
  • CDC's National Program of Cancer Registries uses dictionary-based and cloud-based statistical NLP approaches to process pathology reports. (cdc.gov)
  • Natural Language Processing (NLP) is an incredible technology that allows computers to understand and respond to written and spoken language. (teaco.nl)
  • 1960s: Some notably successful natural language processing systems developed in the 1960s were SHRDLU, a natural language system working in restricted "blocks worlds" with restricted vocabularies, and ELIZA, a simulation of a Rogerian psychotherapist, written by Joseph Weizenbaum between 1964 and 1966. (wikipedia.org)
  • Up to the 1980s, most natural language processing systems were based on complex sets of hand-written rules. (wikipedia.org)
  • In the year 1960, some natural language processing systems developed, SHRDLU , the work of Chomsky and others together on formal language theory and generative syntax. (analyticssteps.com)
  • Advance Market Analytics published a new research publication on "Natural Language Processing Market Insights, to 2027" with 232 pages and enriched with self-explained Tables and charts in presentable format. (openpr.com)
  • A case report in this issue of the AJNR presents the possibility of assessing the localization of language processing with the coincidence of sleep and fMRI during an evaluation for surgery. (ajnr.org)
  • It is our pleasure to announce the Games and Gamification for NLP (Games4NLP) workshop hosted at the 11th edition of the Language Resources and Evaluation Conference (LREC) , 7-12 May 2018, Miyazaki (Japan). (essex.ac.uk)
  • When we ask questions of these virtual assistants, NLP is what enables them to not only understand the user's request, but to also respond in natural language. (oracle.com)
  • How do these devices understand Natural languages like English, Hindi, or French? (indiatimes.com)
  • With the help of natural language processing, a sentiment classifier can understand the complexity of each opinion, comment, and automatically tag them into classified buckets that have been preset. (vikramstudio46.com)
  • Modern computers are capable of deciphering and responding to natural speech. (onpassive.com)
  • What if computers could immaculately translate English to French or over 100 languages from all over the world? (exxactcorp.com)
  • Developers can also access excellent support channels for integration with other languages and tools. (kemeisc.com)
  • The researchers developed natural language processing tools, which they used to scrounge clinical notes for information about which devices patients received. (medscape.com)
  • The large language models (LLMs) are a direct result of the recent advances in machine learning. (teaco.nl)
  • Syntactic investigation and semantic examination are the primary methods used to finish Natural Language Processing undertakings. (digitalvisi.com)
  • A semantic examination is one of the troublesome parts of Natural Language Processing that has not been ultimately settled. (digitalvisi.com)
  • With NLP, online translators can translate languages more accurately and present grammatically-correct results. (elalameya-group.com)
  • Generating natural and contextually relevant language. (kelaax.com)
  • In the beginning of the year 1990s, NLP started growing faster and achieved good process accuracy, especially in English Grammar. (teaco.nl)
  • Over the years, the Machine Learning Center at Georgia Tech (ML@GT) has steadily been increasing its presence in the natural language processing (NLP) community. (gatech.edu)
  • This was due to both the steady increase in computational power (see Moore's law) and the gradual lessening of the dominance of Chomskyan theories of linguistics (e.g. transformational grammar), whose theoretical underpinnings discouraged the sort of corpus linguistics that underlies the machine-learning approach to language processing. (wikipedia.org)
  • Following the preceding steps, the machine will communicate with individuals using their language. (onpassive.com)
  • Natural language processing can be structured in many different ways using various machine learning models, depending on what's being analyzed. (sandiego.edu)
  • The Machine and Deep Learning communities have been actively pursuing Natural Language Processing through various techniques. (acgaudyt.pl)
  • Natural language processing: a machine learning perspective. (lu.se)
  • Natural language processing (NLP) is an interdisciplinary subfield of computer science and linguistics. (wikipedia.org)
  • While a complete summary of natural language processing is well beyond the scope of this article, we will cover some concepts that are commonly used in general purpose natural language processing work. (kdnuggets.com)
  • Reviewing the literature: the text mining system has the ability to process the text, define the theme and subjects, highlight the most commonly used terms or the most popular topics, etc. (sloboda-studio.com)
  • With 10 papers accepted to the main EMNLP conference and three papers at a new, coinciding sister publication called Findings of EMNLP , Georgia Tech researchers are tackling problems like how to summarize text and interpreting how persuasive language impacts domains advertising and argumentation . (gatech.edu)
  • For instance, a funny occurrence happened during the 1950s when interpreting certain words between the English and the Russian languages. (digitalvisi.com)
  • An increasing number of studies have reported using natural language processing (NLP) to assist observational research by extracting clinical information from electronic health records (EHRs). (bvsalud.org)
  • Ross Quillian's successful work on natural language was demonstrated with a vocabulary of only twenty words, because that was all that would fit in a computer memory at the time. (wikipedia.org)
  • Such natural languages are those used in speech by individuals and are distinct from computer programming languages. (justia.com)
  • This is required as language, unlike computer commands, have many nuances. (indiatimes.com)
  • One natural and necessary question that needs to be addressed in this context, both in research projects and in production settings, is the question how ethical the work is, both regarding the process and its outcome. (aclanthology.org)
  • One interesting side-effect of language modeling is getting a generative model that we can use to generate all kinds of sequences. (exxactcorp.com)
  • Pretrained image classification networks have already learned to extract powerful and informative features from natural images. (mathworks.com)
  • Towards this end, we articulate a set of issues, propose a set of best practices, notably a process featuring an ethics review board, and sketch and how they could be meaningfully applied. (aclanthology.org)
  • It also allows you to perform text analysis in multiple languages such as English, French, Chinese, and German. (acgaudyt.pl)
  • In a system which supports multiple interaction languages, multiple message files are needed, thus complicating the burden on the programmer and the user. (justia.com)
  • Combined studies have more recently identified clear evidence for the processing of auditory information and language during NREM sleep. (ajnr.org)