Txt2MEDLINE: text-messaging access to MEDLINE/PubMed.
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We developed a text messaging system for processing incoming Short Message Service (SMS) queries, retrieving medical journal citations from MEDLINE/PubMed and sending them back to the user in the text message format. A database of medical terminology abbreviations and acronyms was developed to reduce the size of text in journal citations and abstracts because of the 160-character per message limit of text messages. Queries may be sent as full-length terms or abbreviations. An algorithm transforms the citations into the SMS format. An abbreviated TBL (the bottom-line) summary instead of the full abstract is sent to the mobile device to shorten the resulting text. The system decreases citation size by 77.5+/-7.9%. Txt2MEDLINE provides physicians and healthcare personnel another rapid and convenient method for searching MEDLINE/PubMed through wireless mobile devices. It is accessible from any location worldwide where GSM wireless service is available. (+info)
A comparative study of supervised learning as applied to acronym expansion in clinical reports.
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Electronic medical records (EMR) constitute a valuable resource of patient specific information and are increasingly used for clinical practice and research. Acronyms present a challenge to retrieving information from the EMR because many acronyms are ambiguous with respect to their full form. In this paper we perform a comparative study of supervised acronym disambiguation in a corpus of clinical notes, using three machine learning algorithms: the naive Bayes classifier, decision trees and Support Vector Machines (SVMs). Our training features include part-of-speech tags, unigrams and bigrams in the context of the ambiguous acronym. We find that the combination of these feature types results in consistently better accuracy than when they are used individually, regardless of the learning algorithm employed. The accuracy of all three methods when using all features consistently approaches or exceeds 90%, even when the baseline majority classifier is below 50%. (+info)
Codes and abbreviations for approved of effectively published names of genera of bacteria published from January 1980 to December 1990.
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Lists of abbreviations for genus names of bacteria are expanded to accommodate 103 new entries which are names that have been validity published since the publication of an updated list by Rogosa et al. in 1986 (Int. J. Syst. Bacteriol. 36:464-472). These abbreviations are provided to serve the need for appropriate codified abbreviations for use in processing or indexing of information on computers. (+info)
PANDAS: a new disease?
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OBJECTIVE: To establish the diagnostic criteria for PANDAS and to analyze the existing evidence regarding its etiopathogenesis, treatment and prophylaxis. SOURCES: Review of the scientific literature through a MEDLINE search carried out between 1989 and 2006. SUMMARY OF THE FINDINGS: The diagnostic criteria for PANDAS were established nearly 10 years ago, but a lot of controversy still exists over the actual existence of this new pediatric disease. The name of this new disease, supposedly of poststreptococcal etiology, derives from an acronym that stands for pediatric autoimmune neuropsychiatric disease associated with streptococcal infection. Tics and obsessive-compulsive symptoms are the major clinical signs of the disease, which develop after streptococcal infections, probably through autoimmune mechanisms. Even though these neuropsychiatric symptoms are common in rheumatic chorea, whose etiology is also poststreptococcal, the classic choreiform movements and other symptoms of rheumatic fevers are absent in PANDAS. The use of antimicrobial and immunologic therapy has been investigated and considered feasible in some cases. CONCLUSIONS: Further research is still necessary in order to answer the question posed in the title of this article. In the meantime, the identification of tic disorders and obsessive-compulsive disorders in children should include the possibility of PANDAS, seeking to provide evidence of previous streptococcal infection. (+info)
Objective surgical skill assessment: the diagonal operating matrix.
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There is an urgent need for structured surgical training and assessment due to the reduction in the training duration with the European Working Time Directive (EWTD). We propose a model for objective skill assessment, the PAR-Diagonal Operating Matrix (PAR-DOM) which breaks down the task of vascular anastomosis into clearly defined skills. The PAR-DOM is made up of a 3x5 table and progress is made along vectors defined on the x-axis as PAR and on the y-axis as four levels. PAR defines three skills at each level. Each skill is graded from 1-3 (this may be taken as below average, average, above average). The skills at various levels are: Level 0 - Posture, Address, Relaxation; Level 1 - Pick-up, Airtime, Rotation; Level 2 - Placing, Angles, Rhythm; Level 3 - Precision, Adaptability, Reproducibility; Level 4 - Pace, Awareness, Relations. The PAR-DOM matrix provides a graphic representation of the progress of trainees over their training period assigned for them to stay with the trainer and also help identify individual strengths and weaknesses. (+info)
The acronym superiority effect.
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The visual world is replete with noisy, continuous, perceptually variant linguistic information, which fluent readers rapidly translate from percept to meaning. What are the properties the language comprehension system uses as cues to initiate lexical/semantic access in response to some, but not all, orthographic strings? In the behavioral, electromagnetic, and neuropsychological literatures, orthographic regularity and familiarity have been identified as critical factors. Here, we present a study in the Reicher-Wheeler tradition that manipulates these two properties independently through the use of four stimulus categories: familiar and orthographically regular words, unfamiliar but regular pseudowords, unfamiliar illegal strings, and familiar but orthographically illegal acronyms. We find that, like letters in words and pseudowords, letters in acronyms enjoy an identification benefit relative to similarly illegal, but unfamiliar strings. This supports theories of visual word recognition in which familiarity, rather than orthographic regularity, plays a critical role in gating processing. (+info)
Enhancing acronym/abbreviation knowledge bases with semantic information.
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OBJECTIVE: In the biomedical domain, a terminology knowledge base that associates acronyms/abbreviations (denoted as SFs) with the definitions (denoted as LFs) is highly needed. For the construction such terminology knowledge base, we investigate the feasibility to build a system automatically assigning semantic categories to LFs extracted from text. METHODS: Given a collection of pairs (SF,LF) derived from text, we i) assess the coverage of LFs and pairs (SF,LF) in the UMLS and justify the need of a semantic category assignment system; and ii) automatically derive name phrases annotated with semantic category and construct a system using machine learning. RESULTS: Utilizing ADAM, an existing collection of (SF,LF) pairs extracted from MEDLINE, our system achieved an f-measure of 87% when assigning eight UMLS-based semantic groups to LFs. The system has been incorporated into a web interface which integrates SF knowledge from multiple SF knowledge bases. Web site: http://gauss.dbb.georgetown.edu/liblab/SFThesurus. (+info)
A study of abbreviations in clinical notes.
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Various natural language processing (NLP) systems have been developed to unlock patient information from narrative clinical notes in order to support knowledge based applications such as error detection, surveillance and decision support. In many clinical notes, abbreviations are widely used without mention of their definitions, which is very different from the use of abbreviations in the biomedical literature. Thus, it is critical, but more challenging, for NLP systems to correctly interpret abbreviations in these notes. In this paper we describe a study of a two-step model for building a clinical abbreviation database: first, abbreviations in a text corpus were detected and then a sense inventory was built for those that were found. Four detection methods were developed and evaluated. Results showed that the best detection method had a precision of 91.4% and recall of 80.3%. A simple method was used to build sense inventories from two different knowledge sources: the Unified Medical Language System (UMLS) and a MEDLINE abbreviation database (ADAM). Evaluation showed the inventory from the UMLS appeared to be the more appropriate of the two for defining the sense of abbreviations, but was not ideal. It covered 35% of the senses and had an ambiguity rate of 40% for those that were covered. However, annotation by domain experts appears necessary for uncovered abbreviations and to determine the correct senses. (+info)