Data processing largely performed by automatic means.
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)
A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.
Automatic, mechanical, and apparently undirected behavior which is outside of conscious control.
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 technique of inputting two-dimensional images into a computer and then enhancing or analyzing the imagery into a form that is more useful to the human observer.
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.
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.
Controlled operations of analytic or diagnostic processes, or systems by mechanical or electronic devices.
Methods developed to aid in the interpretation of ultrasound, radiographic images, etc., for diagnosis of disease.
Computer processing of a language with rules that reflect and describe current usage rather than prescribed usage.
The process of generating three-dimensional images by electronic, photographic, or other methods. For example, three-dimensional images can be generated by assembling multiple tomographic images with the aid of a computer, while photographic 3-D images (HOLOGRAPHY) can be made by exposing film to the interference pattern created when two laser light sources shine on an object.
Computer systems or networks designed to provide radiographic interpretive information.
Application of computer programs designed to assist the physician in solving a diagnostic problem.
Organized activities related to the storage, location, search, and retrieval of information.
The portion of an interactive computer program that issues messages to and receives commands from a user.
Computer-assisted processing of electric, ultrasonic, or electronic signals to interpret function and activity.
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)
Activities performed to identify concepts and aspects of published information and research reports.
Improvement of the quality of a picture by various techniques, including computer processing, digital filtering, echocardiographic techniques, light and ultrastructural MICROSCOPY, fluorescence spectrometry and microscopy, scintigraphy, and in vitro image processing at the molecular level.
Method of analyzing chemicals using automation.
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.
The relationships between symbols and their meanings.
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.
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.
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).
Combination or superimposition of two images for demonstrating differences between them (e.g., radiograph with contrast vs. one without, radionuclide images using different radionuclides, radiograph vs. radionuclide image) and in the preparation of audiovisual materials (e.g., offsetting identical images, coloring of vessels in angiograms).
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.
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.
Approximate, quantitative reasoning that is concerned with the linguistic ambiguity which exists in natural or synthetic language. At its core are variables such as good, bad, and young as well as modifiers such as more, less, and very. These ordinary terms represent fuzzy sets in a particular problem. Fuzzy logic plays a key role in many medical expert systems.
The time from the onset of a stimulus until a response is observed.
Materials used as reference points for imaging studies.
A process that includes the determination of AMINO ACID SEQUENCE of a protein (or peptide, oligopeptide or peptide fragment) and the information analysis of the sequence.
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.
The process of pictorial communication, between human and computers, in which the computer input and output have the form of charts, drawings, or other appropriate pictorial representation.
The act of testing the software for compliance with a standard.
Software designed to store, manipulate, manage, and control data for specific uses.

Archive of mass spectral data files on recordable CD-ROMs and creation and maintenance of a searchable computerized database. (1/1111)

A database containing names of mass spectral data files generated in a forensic toxicology laboratory and two Microsoft Visual Basic programs to maintain and search this database is described. The data files (approximately 0.5 KB/each) were collected from six mass spectrometers during routine casework. Data files were archived on 650 MB (74 min) recordable CD-ROMs. Each recordable CD-ROM was given a unique name, and its list of data file names was placed into the database. The present manuscript describes the use of search and maintenance programs for searching and routine upkeep of the database and creation of CD-ROMs for archiving of data files.  (+info)

LocaLisa: new technique for real-time 3-dimensional localization of regular intracardiac electrodes. (2/1111)

BACKGROUND: Estimation of the 3-dimensional (3D) position of ablation electrodes from fluoroscopic images is inadequate if a systematic lesion pattern is required in the treatment of complex arrhythmogenic substrates. METHODS AND RESULTS: We developed a new technique for online 3D localization of intracardiac electrodes. Regular catheter electrodes are used as sensors for a high-frequency transthoracic electrical field, which is applied via standard skin electrodes. We investigated localization accuracy within the right atrium, right ventricle, and left ventricle by comparing measured and true interelectrode distances of a decapolar catheter. Long-term stability was analyzed by localization of the most proximal His bundle before and after slow pathway ablation. Electrogram recordings were unaffected by the applied electrical field. Localization data from 3 catheter positions, widely distributed within the right atrium, right ventricle, or left ventricle, were analyzed in 10 patients per group. The relationship between measured and true electrode positions was highly linear, with an average correlation coefficient of 0.996, 0.997, and 0.999 for the right atrium, right ventricle, and left ventricle, respectively. Localization accuracy was better than 2 mm, with an additional scaling error of 8% to 14%. After 2 hours, localization of the proximal His bundle was reproducible within 1.4+/-1.1 mm. CONCLUSIONS: This new technique enables accurate and reproducible real-time localization of electrode positions in cardiac mapping and ablation procedures. Its application does not distort the quality of electrograms and can be applied to any electrode catheter.  (+info)

Using a multidisciplinary automated discharge summary process to improve information management across the system. (3/1111)

We developed and implemented an automated discharge summary process in a regional integrated managed health system. This multidisciplinary effort was initiated to correct deficits in patients' medical record documentation involving discharge instructions, follow-up care, discharge medications, and patient education. The results of our team effort included an automated summary that compiles data entered via computer pathways during a patient's hospitalization. All information regarding admission medications, patient education, follow-up care, referral at discharge activities, diagnosis, and other pertinent medical events are formulated into the discharge summary, discharge orders, patient discharge instructions, and transfer information as applicable. This communication process has tremendously enhanced information management across the system and helps us maintain complete and thorough documentation in patient records.  (+info)

Mapping of atrial activation with a noncontact, multielectrode catheter in dogs. (4/1111)

BACKGROUND: Endocardial mapping of sustained arrhythmias has traditionally been performed with a roving diagnostic catheter. Although this approach is adequate for many tachyarrhythmias, it has limitations. The purpose of this study was to evaluate a novel noncontact mapping system for assessing atrial tachyarrhythmias. METHODS AND RESULTS: The mapping system consists of a 9F multielectrode-array balloon catheter that has 64 active electrodes and ring electrodes for emitting a locator signal. The locator signal was used to construct a 3-dimensional right atrial map; it was independently validated and was highly accurate. Virtual electrograms were calculated at 3360 endocardial sites in the right atrium. We evaluated right atrial activation by positioning the balloon catheter in the mid right atrium via a femoral venous approach. Experiments were performed on 12 normal mongrel dogs. The mean correlation coefficient between contact and virtual electrograms was 0.80+/-0.12 during sinus rhythm. Fifty episodes of atrial flutter induced in 11 animals were evaluated. In the majority of experiments, complete or almost complete reentrant circuits could be identified within the right atrium. Mean correlation coefficient between virtual and contact electrograms was 0.85+/-0.17 in atrial flutter. One hundred fifty-six episodes of pacing-induced atrial fibrillation were evaluated in 11 animals. Several distinct patterns of right atrial activation were seen, including single-activation wave fronts and multiple simultaneous-activation wave fronts. Mean correlation coefficient between virtual and contact electrograms during atrial fibrillation was 0.81+/-0.18. The accuracy of electrogram reconstruction was lower at sites >4.0 cm from the balloon center and at sites with a high spatial complexity of electrical activation. CONCLUSIONS: This novel noncontact mapping system can evaluate conduction patterns during sinus rhythm, demonstrate reentry during atrial flutter, and describe right atrial activation during atrial fibrillation. The accuracy of electrogram reconstruction was good at sites <4.0 cm from the balloon center, and thus the system has the ability to perform high-resolution multisite mapping of atrial tachyarrhythmias in vivo.  (+info)

Radiation-induced leukocyte entrapment in the rat retinal microcirculation. (5/1111)

PURPOSE: To evaluate the effects of irradiation on leukocyte dynamics in the rat retinal microcirculation. METHODS: Thirty-five Brown-Norway rats received a dose of 10 Gy irradiation in one fraction. Leukocyte dynamics were studied with acridine orange digital fluorography, in which a nuclear fluorescent dye of acridine orange is used and examined by scanning laser ophthalmoscope. This technique allowed visualization of fluorescent leukocytes in vivo. The leukocyte dynamics were evaluated at 0, 4, 7, 14, 30, and 60 days after the irradiation. RESULTS: Mean leukocyte velocity in the retinal capillaries decreased immediately. It partially recovered on day 4 but then gradually decreased up to the 2-month mark. Low-dose irradiation induced entrapment of leukocytes in the retinal microcirculation. The number of entrapped leukocytes gradually increased with time. The major retinal vessels significantly constricted immediately after irradiation. The diameter was reduced by 76% in arteries and 75% in veins, 2 months after irradiation. CONCLUSIONS: Entrapped leukocytes may be activated and exacerbate vascular injury and microinfarction and thus may participate in the pathogenesis of radiation retinopathy.  (+info)

Shift work-related problems in 16-h night shift nurses (1): Development of an automated data processing system for questionnaires, heart rate, physical activity and posture. (6/1111)

To assess the shift work-related problems associated with a 16-h night shift in a two-shift system, we took the following important factors into consideration; the interaction between circadian rhythms and the longer night shift, the type of morningness and eveningness experienced, the subjective sleep feeling, the subjects' daily behavior, the effectiveness of taking a nap during the long night shift, and finally the effectiveness of using several different kinds of measuring devices. Included among the measuring devices used were a standard questionnaire, repetitive self-assessment of subjective symptoms and daily behavior at short intervals, and a continuous recording of such objective indices as physical activity and heart rate. A potential problem lies in the fact that field studies that use such measures tend to produce a mass of data, and are thus faced with the accompanying technical problem of analyzing such a large amount of data (time, effort and cost). To solve the data analysis problem, we developed an automated data processing system. Through the use of an image scanner with a paper feeder, standard paper, an optical character recognition function and common application software, we were able to analyze a mass of data continuously and automatically within a short time. Our system should prove useful for field studies that produce a large amount of data collected with several different kinds of measuring devices.  (+info)

Use of bar code readers and programmable keypads to improve the speed and accuracy of manual data entry in the clinical microbiology laboratory: experience of two laboratories. (7/1111)

AIM: To assess the effect of the use of bar code readers and programmable keypads for entry of specimen details and results in two microbiology laboratories. METHODS: The solutions selected in each laboratory are described. The benefits resulting from the implementation were measured in two ways. The speed of data entry and error reduction were measured by observation. A questionnaire was completed by users of bar codes. RESULTS: There were savings in time and in reduced data entry errors. Average time to enter a report by keyboard was 21.1 s v 14.1 s for bar coded results entry. There were no observed errors with the bar code readers but 55 errors with keystroke entries. The laboratory staff of all grades found the system fast, easy to use, and less stressful than conventional keyboard entry. CONCLUSIONS: Indirect time savings should accrue from the observed reduction in incorrectly entered data. Any microbiology laboratory seeking to improve the accuracy and efficiency of data entry into their laboratory information systems should consider the adoption of this technology which can be readily interfaced to existing terminals.  (+info)

ProbeDesigner: for the design of probesets for branched DNA (bDNA) signal amplification assays. (8/1111)

MOTIVATION: The sensitivity and specificity of branched DNA (bDNA) assays are derived in part through the judicious design of the capture and label extender probes. To minimize non-specific hybridization (NSH) events, which elevate assay background, candidate probes must be computer screened for complementarity with generic sequences present in the assay. RESULTS: We present a software application which allows for rapid and flexible design of bDNA probesets for novel targets. It includes an algorithm for estimating the magnitude of NSH contribution to background, a mechanism for removing probes with elevated contributions, a methodology for the simultaneous design of probesets for multiple targets, and a graphical user interface which guides the user through the design steps. AVAILABILITY: The program is available as a commercial package through the Pharmaceutical Drug Discovery program at Chiron Diagnostics.  (+info)

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.

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.

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.

Automatism is a medical and legal term that refers to unconscious or involuntary behavior or actions that are performed without conscious awareness or control. In medicine, automatisms can occur in various neurological or psychiatric conditions, such as epilepsy, sleepwalking, or certain mental disorders. During an automatism episode, a person may appear to be awake and functioning, but they are not fully aware of their actions and may not remember them later.

In the legal context, automatism is often used as a defense in criminal cases, where it is argued that the defendant was not mentally responsible for their actions due to an involuntary automatism episode. However, the definition and application of automatism as a legal defense can vary depending on the jurisdiction and the specific circumstances of the case.

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!

Computer-assisted image processing is a medical term that refers to the use of computer systems and specialized software to improve, analyze, and interpret medical images obtained through various imaging techniques such as X-ray, CT (computed tomography), MRI (magnetic resonance imaging), ultrasound, and others.

The process typically involves several steps, including image acquisition, enhancement, segmentation, restoration, and analysis. Image processing algorithms can be used to enhance the quality of medical images by adjusting contrast, brightness, and sharpness, as well as removing noise and artifacts that may interfere with accurate diagnosis. Segmentation techniques can be used to isolate specific regions or structures of interest within an image, allowing for more detailed analysis.

Computer-assisted image processing has numerous applications in medical imaging, including detection and characterization of lesions, tumors, and other abnormalities; assessment of organ function and morphology; and guidance of interventional procedures such as biopsies and surgeries. By automating and standardizing image analysis tasks, computer-assisted image processing can help to improve diagnostic accuracy, efficiency, and consistency, while reducing the potential for human error.

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.

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.

Automation in a laboratory refers to the use of technology and machinery to automatically perform tasks that were previously done manually by lab technicians or scientists. This can include tasks such as mixing and dispensing liquids, tracking and monitoring experiments, and analyzing samples. Automation can help increase efficiency, reduce human error, and allow lab personnel to focus on more complex tasks.

There are various types of automation systems used in laboratory settings, including:

1. Liquid handling systems: These machines automatically dispense precise volumes of liquids into containers or well plates, reducing the potential for human error and increasing throughput.
2. Robotic systems: Robots can be programmed to perform a variety of tasks, such as pipetting, centrifugation, and incubation, freeing up lab personnel for other duties.
3. Tracking and monitoring systems: These systems automatically track and monitor experiments, allowing scientists to remotely monitor their progress and receive alerts when an experiment is complete or if there are any issues.
4. Analysis systems: Automated analysis systems can quickly and accurately analyze samples, such as by measuring the concentration of a particular molecule or identifying specific genetic sequences.

Overall, automation in the laboratory can help improve accuracy, increase efficiency, and reduce costs, making it an essential tool for many scientific research and diagnostic applications.

Computer-assisted image interpretation is the use of computer algorithms and software to assist healthcare professionals in analyzing and interpreting medical images. These systems use various techniques such as pattern recognition, machine learning, and artificial intelligence to help identify and highlight abnormalities or patterns within imaging data, such as X-rays, CT scans, MRI, and ultrasound images. The goal is to increase the accuracy, consistency, and efficiency of image interpretation, while also reducing the potential for human error. It's important to note that these systems are intended to assist healthcare professionals in their decision making process and not to replace them.

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.

Three-dimensional (3D) imaging in medicine refers to the use of technologies and techniques that generate a 3D representation of internal body structures, organs, or tissues. This is achieved by acquiring and processing data from various imaging modalities such as X-ray computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, or confocal microscopy. The resulting 3D images offer a more detailed visualization of the anatomy and pathology compared to traditional 2D imaging techniques, allowing for improved diagnostic accuracy, surgical planning, and minimally invasive interventions.

In 3D imaging, specialized software is used to reconstruct the acquired data into a volumetric model, which can be manipulated and viewed from different angles and perspectives. This enables healthcare professionals to better understand complex anatomical relationships, detect abnormalities, assess disease progression, and monitor treatment response. Common applications of 3D imaging include neuroimaging, orthopedic surgery planning, cancer staging, dental and maxillofacial reconstruction, and interventional radiology procedures.

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.

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.

'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.

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.

Computer-assisted signal processing is a medical term that refers to the use of computer algorithms and software to analyze, interpret, and extract meaningful information from biological signals. These signals can include physiological data such as electrocardiogram (ECG) waves, electromyography (EMG) signals, electroencephalography (EEG) readings, or medical images.

The goal of computer-assisted signal processing is to automate the analysis of these complex signals and extract relevant features that can be used for diagnostic, monitoring, or therapeutic purposes. This process typically involves several steps, including:

1. Signal acquisition: Collecting raw data from sensors or medical devices.
2. Preprocessing: Cleaning and filtering the data to remove noise and artifacts.
3. Feature extraction: Identifying and quantifying relevant features in the signal, such as peaks, troughs, or patterns.
4. Analysis: Applying statistical or machine learning algorithms to interpret the extracted features and make predictions about the underlying physiological state.
5. Visualization: Presenting the results in a clear and intuitive way for clinicians to review and use.

Computer-assisted signal processing has numerous applications in healthcare, including:

* Diagnosing and monitoring cardiac arrhythmias or other heart conditions using ECG signals.
* Assessing muscle activity and function using EMG signals.
* Monitoring brain activity and diagnosing neurological disorders using EEG readings.
* Analyzing medical images to detect abnormalities, such as tumors or fractures.

Overall, computer-assisted signal processing is a powerful tool for improving the accuracy and efficiency of medical diagnosis and monitoring, enabling clinicians to make more informed decisions about patient care.

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.

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.

Image enhancement in the medical context refers to the process of improving the quality and clarity of medical images, such as X-rays, CT scans, MRI scans, or ultrasound images, to aid in the diagnosis and treatment of medical conditions. Image enhancement techniques may include adjusting contrast, brightness, or sharpness; removing noise or artifacts; or applying specialized algorithms to highlight specific features or structures within the image.

The goal of image enhancement is to provide clinicians with more accurate and detailed information about a patient's anatomy or physiology, which can help inform medical decision-making and improve patient outcomes.

"Autoanalysis" is not a term that is widely used in the medical field. However, in psychology and psychotherapy, "autoanalysis" refers to the process of self-analysis or self-examination, where an individual analyzes their own thoughts, feelings, behaviors, and experiences to gain insight into their unconscious mind and understand their motivations, conflicts, and emotional patterns.

Self-analysis can involve various techniques such as introspection, journaling, meditation, dream analysis, and reflection on past experiences. While autoanalysis can be a useful tool for personal growth and self-awareness, it is generally considered less reliable and comprehensive than professional psychotherapy or psychoanalysis, which involves a trained therapist or analyst who can provide objective feedback, interpretation, and guidance.

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.

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 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. 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.

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!

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.

The "subtraction technique" is not a widely recognized or established term in medical terminology. It may refer to various methods used in different medical contexts that involve subtracting or comparing measurements, values, or observations to diagnose, monitor, or treat medical conditions. However, without more specific context, it's difficult to provide an accurate medical definition of the term.

In radiology, for example, the subtraction technique is a method used in imaging to enhance the visibility of certain structures by digitally subtracting one image from another. This technique is often used in angiography to visualize blood vessels more clearly.

Therefore, it's essential to provide more context or specify the medical field when using the term "subtraction technique" to ensure accurate communication and understanding.

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.

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.

I'm sorry for any confusion, but "Fuzzy Logic" is actually a term from the field of mathematics and computer science, not medicine. It is a form of many-valued logic that deals with reasoning that is approximate rather than fixed and exact. In contrast to traditional logic, where binary sets have distinct boundaries (true or false, 0 or 1, etc.), fuzzy logic allows for continuous values between 0 and 1, making it particularly useful in areas where precise definitions are difficult, such as medical diagnosis or robotics.

Reaction time, in the context of medicine and physiology, refers to the time period between the presentation of a stimulus and the subsequent initiation of a response. This complex process involves the central nervous system, particularly the brain, which perceives the stimulus, processes it, and then sends signals to the appropriate muscles or glands to react.

There are different types of reaction times, including simple reaction time (responding to a single, expected stimulus) and choice reaction time (choosing an appropriate response from multiple possibilities). These measures can be used in clinical settings to assess various aspects of neurological function, such as cognitive processing speed, motor control, and alertness.

However, it is important to note that reaction times can be influenced by several factors, including age, fatigue, attention, and the use of certain medications or substances.

Fiducial markers, also known as fiducials, are small markers that are often used in medical imaging to help identify and target specific locations within the body. These markers can be made of various materials, such as metal or plastic, and are typically placed at or near the site of interest through a minimally invasive procedure.

In radiation therapy, fiducial markers are often used to help ensure that the treatment is accurately targeted to the correct location. The markers can be seen on imaging scans, such as X-rays or CT scans, and can be used to align the treatment beam with the target area. This helps to improve the precision of the radiation therapy and reduce the risk of harm to surrounding healthy tissue.

Fiducial markers may also be used in other medical procedures, such as image-guided surgery or interventional radiology, to help guide the placement of instruments or devices within the body.

Protein sequence analysis is the systematic examination and interpretation of the amino acid sequence of a protein to understand its structure, function, evolutionary relationships, and other biological properties. It involves various computational methods and tools to analyze the primary structure of proteins, which is the linear arrangement of amino acids along the polypeptide chain.

Protein sequence analysis can provide insights into several aspects, such as:

1. Identification of functional domains, motifs, or sites within a protein that may be responsible for its specific biochemical activities.
2. Comparison of homologous sequences from different organisms to infer evolutionary relationships and determine the degree of similarity or divergence among them.
3. Prediction of secondary and tertiary structures based on patterns of amino acid composition, hydrophobicity, and charge distribution.
4. Detection of post-translational modifications that may influence protein function, localization, or stability.
5. Identification of protease cleavage sites, signal peptides, or other sequence features that play a role in protein processing and targeting.

Some common techniques used in protein sequence analysis include:

1. Multiple Sequence Alignment (MSA): A method to align multiple protein sequences to identify conserved regions, gaps, and variations.
2. BLAST (Basic Local Alignment Search Tool): A widely-used tool for comparing a query protein sequence against a database of known sequences to find similarities and infer function or evolutionary relationships.
3. Hidden Markov Models (HMMs): Statistical models used to describe the probability distribution of amino acid sequences in protein families, allowing for more sensitive detection of remote homologs.
4. Protein structure prediction: Methods that use various computational approaches to predict the three-dimensional structure of a protein based on its amino acid sequence.
5. Phylogenetic analysis: The construction and interpretation of evolutionary trees (phylogenies) based on aligned protein sequences, which can provide insights into the historical relationships among organisms or proteins.

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.

Computer graphics is the field of study and practice related to creating images and visual content using computer technology. It involves various techniques, algorithms, and tools for generating, manipulating, and rendering digital images and models. These can include 2D and 3D modeling, animation, rendering, visualization, and image processing. Computer graphics is used in a wide range of applications, including video games, movies, scientific simulations, medical imaging, architectural design, and data visualization.

Software validation, in the context of medical devices and healthcare, is the process of evaluating software to ensure that it meets specified requirements for its intended use and that it performs as expected. This process is typically carried out through testing and other verification methods to ensure that the software functions correctly, safely, and reliably in a real-world environment. The goal of software validation is to provide evidence that the software is fit for its intended purpose and complies with relevant regulations and standards. It is an important part of the overall process of bringing a medical device or healthcare technology to market, as it helps to ensure patient safety and regulatory compliance.

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.

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  • 4 (1) of the European Regulation (EU) 2016/679 (General Data Protection Regulation, " GDPR "), relating to an identified or identifiable natural person like names, addresses, email addresses, or phone numbers, as well as other non-public information that is associated with the foregoing. (
  • The EU's General Data Protection Regulation ( GDPR ) and Brazil's General Data Protection Law ( LGPD ) set out how companies can lawfully process someone's personal data. (
  • Finally, the GDPR applies to any business handling data belonging to an EU resident. (
  • The LGPD and GDPR specifically protect children's data, but there's one key difference. (
  • The age of consent for data collection under the GDPR is 16. (
  • While the LGPD sets out 10 specific principles for ethical data processing, the GDPR only has seven. (
  • Alvarado, D , Johnston, B & Brown, C 2022, ' Automatic extraction of pharmaceutical manufacturing data from patents using Natural Language Processing (NLP) ', CMAC Annual Open Day 2022, Glasgow, United Kingdom, 16/05/22 - 18/05/22 pp. 27-27. (
  • The development of both processing technologies and data visualization and analysis methods today make it possible to provide almost seamless integration of data acquisition and analysis systems. (
  • Why don't we take particular notice within the several automatic methods available in the market. (
  • Our method is more automatic and efficient than previous automatic augmentation methods, which still rely on pre-defined operations with human-specified ranges and costly bi-level optimization. (
  • METHODS: Data from the National Health and Nutrition Examination Survey (NHANES) between 2005 and 2008 were utilized for this cross-sectional analysis. (
  • As a data subject, you have different rights, including a right to access, rectification, erasure, restriction of processing and data portability with regard to your Personal Information. (
  • Automatic keyword retrieval from clinical texts: an application of natural language processing to massive data of Chilean suspected diagnosis]. (
  • We describe how to implement a laboratory data-based surveillance system in a clinical microbiology laboratory. (
  • The data were then sorted and used to execute the following 2 surveillance systems in Excel: the Bacterial real-time Laboratory-based Surveillance System (BALYSES) for monitoring the number of patients infected with bacterial species isolated at least once in our laboratory during the study periodl and the Marseille Antibiotic Resistance Surveillance System (MARSS), which surveys the primary β-lactam resistance phenotypes for 15 selected bacterial species. (
  • On the basis of our experience at the Assistance Publique-Hôpitaux de Marseille (AP-HM), we describe all the steps necessary for implementing a laboratory data-based syndromic surveillance system in a laboratory. (
  • The analysis of NHANES 2005-2006 laboratory data must be conducted with the key survey design and basic demographic variables. (
  • regarding indicators and quality specifications for the non-analytical processes in laboratory medicine. (
  • Key processes are the core of laboratory activity port processes (client relationships, instrument and and include pre-analytic, analytic, and post-analytic infrastructure maintenance, safety and risk preven- processes. (
  • These difficulties are associated with photogrammetric processing of images and visualization of the results obtained on the web. (
  • The Azure Data Explorer toolbox gives you an end-to-end solution for data ingestion, query, visualization, and management. (
  • Data visualization helps you gain important insights. (
  • Azure Data Explorer offers built-in visualization and dashboarding out of the box, with support for various charts and visualizations. (
  • By its decision of July 26, 2017, the Dutch Supreme Court ( Court ) ruled that the Dutch tax authority is required to delete the personal data that it had obtained through Automatic Number Plate Recognition cameras ( ANPR data ). (
  • The appellant argued that the tax authorities had no legal ground for processing its personal data, and that doing so constituted an infringement upon its right to privacy. (
  • The State Secretary argued that the ANPR data at subject did not constitute personal data as the license plate pertaining to the ANPR data was not registered under an individual human being, but under a company. (
  • The Court found that because the ANPR data can easily be led back to the appellant, it does indeed qualify as personal data. (
  • Additionally, also they are quite adaptable and adaptable so that you can have used them to accomplish his personal data handling and analysis. (
  • Personal Information " means any information, including personal data within the meaning of Art. (
  • d) „controller of the file" means the natural or legal person, public authority, agency or any other body who is competent according to the national law to decide what should be the purpose of the automated data file, which categories of personal data should be stored and which operations should be applied to them. (
  • 1. The Parties undertake to apply this convention to automated personal data files and automatic processing of personal data in the public and private sectors. (
  • a) that it will not apply this convention to certain categories of automated personal data files, a list of which will be deposited. (
  • c) that it will also apply this convention to personal data files which are not processed automatically. (
  • b) or c) above may give notice in the said declaration that such extensions shall apply only to certain categories of personal data files, a list of which will be deposited. (
  • 4. Any Party which has excluded certain categories of automated personal data files by a declaration provided for in sub-paragraph 2. (
  • We respect your privacy and are committed to protecting your personal data. (
  • Information on disclosure of personal data or of sets of personal data may be found in the " Disclosure of Your Personal Information " section below. (
  • We may process different kinds of personal data about you, depending on whether you chose to create an account with us. (
  • They both apply to personal data processing in the public and private sector. (
  • Each Act recognizes there's some leeway for personal data processing connected to e.g. journalistic or scientific purposes. (
  • According to both Acts, personal data is any information you can use to specifically identify a natural, living person e.g. a name or email address. (
  • Some personal data is especially sensitive. (
  • In both cases, controllers essentially decide what happens to personal data and why it's collected. (
  • The second one is that, on the background of augmenting discontent over `too much openness' the process of Europeanisation in the area of protection and processing of personal data is also accelerated. (
  • The student accesses the website, surprised by the generous amount of personal data about this person, made public for everyone to see. (
  • Data augmentation has proved extremely useful by increasing training data variance to alleviate overfitting and improve deep neural networks' generalization performance. (
  • To automate medical data augmentation, we propose a regularized adversarial training framework via two min-max objectives and three differentiable augmentation models covering affine transformation, deformation, and appearance changes. (
  • Automatic speech recognition is the first challenge in the whole chain. (
  • To focus on automatic emotion processing and sidestep explicit concept-based emotion recognition, participants performed an unrelated target detection task presented in a different sensory modality than the stimulus. (
  • Data is often massive ("Big Data"), produced in large quantities by humans or systems such as satellite systems, social networks, medical imagery, sensors and video surveillance systems. (
  • Streamlining your data flow frees up time for analysis. (
  • Another strategy, known as syndromic surveillance, consists of developing real-time surveillance systems capable of detecting abnormal epidemiologic events, not on the basis of infectious disease diagnosis data, but rather on the basis of nonspecific health indicators, such as absenteeism, chief complaints, and prescription drug sales ( 5 , 9 ). (
  • The NHANES 2005-2006 Household Questionnaire Data Files contain demographic data, health indicators, and other related information collected during household interviews. (
  • Support processes refer to activity that makes pos- specification for the indicators stated. (
  • The aim of the second part of this study is to define been set equal to zero events, such as serious inci- the most appropriate performance indicators for the dents in the infrastructure maintenance process and strategic and support processes of our laboratories number of work accidents in the safety and risk pre- and determine the quality specifications for these vention process. (
  • A methodology for the effective acquisition and processing of data is presented. (
  • In addition, the results of the data assessment demonstrate the repeatability of the data acquisition process, which is a key factor when qualitative analysis is performed. (
  • On-premises data processing linked to high-volume data traffic allows access to the outputs of its online processing. (
  • You have the right to obtain access to your Personal Information that we process. (
  • In some cases, we process usernames or similar identifiers, and email addresses as part of the process to verify that you are over the age of majority required to have access to the Website and to view their contents. (
  • We process browser and operating system information, devices you use to access the Website and your time zone setting. (
  • Access the definitive source for exclusive data-driven insights on today's working world. (
  • The accessibility of these certificates was exploited by many, including a new type of search service that extracts people's data from public registers compiles it and publishes it online, available for everyone to access. (
  • There is also the possibility to find out how much he is earning, but you need to pay a subscription fee to get access to this category of data. (
  • The Epigenetic Epidemiology Publications Database (EEPD) provides access to a knowledge base that is continuously updated using an automated process based on machine learning. (
  • Detailed specimen collection and processing instructions are discussed in the NHANES LPM. (
  • Detailed instructions on specimen collection and processing can be found on the NHANES website. (
  • Construction of text resources for automatic identification of clinical information in unstructured narratives]. (
  • Improve process understanding through the utilisation of latent variables that may be correlated to process parameters. (
  • Potential Benefits of DGM- Aid process design by generating a feasible chain of unit operations for the production of an API/dosage forms- Improve process understanding through the utilisation of latent variables that may be correlated to process parameters. (
  • Both Acts include parameters for data controllers and processors. (
  • Simplify and unify your HCM compliance processes. (
  • Simplify transfer data from all your instruments to your data management system with a single interface. (
  • Similarities When doing explicit data conversions, CAST and CONVERT are available to you. (
  • Air-traffic management is a dedicated domain where in addition to using the voice signal, other contextual information (i.e. air traffic surveillance data, meteorological data, etc.) plays an important role. (
  • On the other hand, emotion signals like for example an aggressive gesture, trigger rapid automatic behavioral responses and this may take place before or independently of full abstract representation of the emotion. (
  • Read the LABDOC file for detailed data processing and editing protocols. (
  • Automatic data processing (ADP) may refer to: Automatic Data Processing, a computing services company. (
  • Cleverbridge Financial Services GmbH, Gereonstrasse 43-65, 50670 Cologne, Germany (" cbFS ") may prepare and process payment transactions as an independent controller of Personal Information for the purpose of preparing, verifying and performing an e-commerce payment transaction conducted on the Store or a storefront that Cleverbridge operates for a seller. (
  • During this process, cbFS receives customer and payment information from the seller, performs fraud prevention services and passes on Personal Information (including payment data) to a specific payment provider. (
  • We process information about how you use our Website, products and services and interact with our content and advertisements, including the pages you visit in our Website, search history, and the referring web page from which you arrived at our Website. (
  • We also offer dedicated validation documentation in just a few steps to support your validation and qualification process - performed by our highly qualified services colleagues, on request. (
  • These authors affirmed that the radiographic processing must be monitored continuously and systematically, because if any failure occurs, this can lead to problems both in the image and the diagnosis. (
  • Connect instruments from Anton Paar and other vendors, and capture all the generated data using your local network infrastructure. (
  • By analyzing structured, semi-structured, and unstructured data across time series, and by using Machine Learning, Azure Data Explorer makes it simple to extract key insights, spot patterns and trends, and create forecasting models. (
  • You can query petabytes of data, with results returned within milliseconds to seconds. (
  • Based on the results it was possible to conclude that there was a ranging in the grey level, because of either the processing degree of the image on the four areas evaluated of each film or at the four temperatures studied which were statistically significant. (
  • The results indicate that the evaluation of the digital image by Digora for Windows 1.5.1 software is efficient and allows the quality control of the radiographic processing properly, showing that the variation of the grey levels indicate the consuming of action of the processing solutions. (
  • According to Thorogood & Horner 1 , the inadequate processing of the radiographic film results in a radiographic image of low quality confirmed by the excessive number of repetition of examinations and also contributing for the increasing of the x-ray exposure for the patient. (
  • Data collection using unmanned systems is an operational and up-to-date source of information about the area. (
  • Neither Act applies to data collection for purely personal or domestic purposes e.g. an address book. (
  • The System Global Area (SGA) Memory Structures in data. (
  • More than 60,000 saline water analyses were collected by the federal Bureau of Mines for entry into automatic data processing system. (
  • The Elecsys 1010 analyzer is a fully automatic run-oriented analyzer system for the determination of immunological tests using the ECL/Origen electrochemiluminescent process. (
  • To verify the possibility of using the digital imaging as a control of the conditions of automatic radiographic processing. (
  • Further processing usually requires transforming the recognized word sequence into the conceptual form, a more important application in ATM. (
  • The ingestion wizard makes the data ingestion process easy, fast, and intuitive. (
  • The Azure Data Explorer web UI provides an intuitive and guided experience that helps you ramp-up quickly to start ingesting data, creating database tables, and mapping structures. (
  • The use of three-dimensional data is integrated in an open source mesh processing tool, thus showing that a spatio-temporal analysis can be performed in a very intuitive way using off-the-shelf or free/open digital tools. (
  • Building the ideal team takes time, but ADP Workforce Now streamlines the process and unlocks the potential of your people. (
  • You can withdraw your consent to the processing of your Personal Information by us at any time. (
  • These raw data are generally transformed into an elaborate form such as relational or multi-dimensional tables, matrix combinations, inverted files or indexes, uni-varied or multi-varied time series, graphs or hypergraphs. (
  • The Finance Division has reduced systems and processing lags and is processing accounts in one-half the time that was required a year ago. (
  • Azure Data Explorer is a fully managed, high-performance, big data analytics platform that makes it easy to analyze high volumes of data in near real time. (
  • Do you need to ingest massive amounts of data in near real-time? (
  • Use Azure Data Explorer for time series analysis with a large set of functions including: adding and subtracting time series, filtering, regression, seasonality detection, geospatial analysis, anomaly detection, scanning, and forecasting. (
  • Time series functions are optimized for processing thousands of time series in seconds. (
  • It enables one time or a continuous ingestion from various sources and in various data formats. (
  • Be sure to confer with your team on each item response, but, for accuracy and precision, rely on the designated data manager to actually input the data. (
  • Atomic coordinates and structure factors for the reported crystal structures have been deposited in the Protein Data Bank under accession numbers 3RIZ for the unliganded BRI1 ectodomain and 3RJ0 for the BRI1-brassinolide complex. (
  • It uses software programs, data enter, output and information to use the characteristics of the many projects. (
  • This applies in particular even if such information is included in our brochures or price lists or we provide information about technical data and the characteristics of the goods on our informational pages in the internet. (
  • by International Food Data Systems Project. (
  • The research the SIG team carries out is centered on the "data", which is a core component of modern information systems. (
  • The conception of the project can allow the conclusions of the analysis by means of AI and other tools to be applied automatically on the process in the form of corrections of drift or with supervision of the specialists. (
  • The Value of the data is then exploited by data analysis and data mining algorithms, machine learning and deep learning to bring out and discover the knowledge it hiddes. (
  • Taking into account the purposes of the processing, you have the right to have incomplete Personal Information completed, including by means of providing a supplementary statement. (
  • You have the right to obtain from us the deletion of Personal Information concerning you, unless the processing is necessary for exercising the right of freedom of expression and information, for compliance with a legal obligation, for reasons of public interest or for the establishment, exercise or defense of legal claims. (
  • According to Article 9 , you can process this data if you get express consent, if there's a legitimate business interest to do so, or under other somewhat-extreme circumstances (such as to protect the public from risk). (
  • Our research covers the entire data processing chain, from raw data to elaborated data accessible to users who seek for information, wish to visualise it, or perform decisional, exploratory and predictive analyses. (
  • Specifically, we process internet protocol (IP) address information, and we set cookies as explained below in the section on Cookies and Automatic Data Processing Technologies. (
  • Values have sible the efficiency and effectiveness of the strate- been stratified according to primary care and hospital gic and key processes. (
  • Automatic instruments mostly are helpful to procedure data over the software application. (
  • There are several businesses that offer you automatic software package at cost-effective prices. (
  • A number of the firms that supply automatic software package employ a end user-pleasant screen, to ensure that obviously any good rookie are able to use this software. (
  • At daily basis, the Quality Control employs the sensitometer and photodensitometer which has been described as "gold standard" of the automatic processing of the films in the radiographic clinics, so that these authors suggest the construct of six graphs of following-up of radiographic processing performance. (
  • Cleverbridge is the controller of the Personal Information that is being processed when you visit the Site or the Store, or when you order Products. (
  • cbFS is the controller of the Personal Information that is being processed during an e-commerce payment transaction conducted on the Store or a storefront that Cleverbridge operates for a seller. (
  • These power tools make it easier to process numerous files and documents. (
  • In this list it shall not include, however, categories of automated data files subject under its domestic law to data protection provisions. (
  • extensive skill is not necessary, but a basic working knowledge of the program (e.g., opening and closing files, entering macros, and entering data) facilitates the use of the tool. (
  • a) Detection: We will develop speech processing techniques for early detection of conditions that impact on speech production. (
  • These exceptions fall under legal grounds for processing, which we'll see below . (
  • The data classes are, with few exceptions, for internal use. (
  • Query Azure Data Explorer with the Kusto Query Language (KQL) , an open-source language initially invented by the team. (
  • Pseudonymising data basically means processing it in such a way that you can't identify the subject anymore. (
  • Machine learning interatomic potentials (MLIPs) are routinely used atomic simulations, but generating databases of atomic configurations used in fitting these models is a laborious process, requiring significant computational and human effort. (
  • 3. Re-processed and retrieved all data for Human Genome Epidemiology databases including Epigenetic Epidemiology data. (