| Automated non-alphanumeric symbol resolution in clinical texts. | |
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MedLine Citation:
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PMID: 22195157 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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Although clinical texts contain many symbols, relatively little attention has been given to symbol resolution by medical natural language processing (NLP) researchers. Interpreting the meaning of symbols may be viewed as a special case of Word Sense Disambiguation (WSD). One thousand instances of four common non-alphanumeric symbols ('+', '-', '/', and '#') were randomly extracted from a clinical document repository and annotated by experts. The symbols and their surrounding context, in addition to bag-of-Words (BoW), and heuristic rules were evaluated as features for the following classifiers: Naïve Bayes, Support Vector Machine, and Decision Tree, using 10-fold cross-validation. Accuracies for '+', '-', '/', and '#' were 80.11%, 80.22%, 90.44%, and 95.00% respectively, with Naïve Bayes. While symbol context contributed the most, BoW was also helpful for disambiguation of some symbols. Symbol disambiguation with supervised techniques can be implemented with reasonable accuracy as a module for medical NLP systems. |
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Authors:
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Sungrim Moon; Serguei Pakhomov; James Ryan; Genevieve B Melton |
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Publication Detail:
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Type: Journal Article Date: 2011-10-22 |
Journal Detail:
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Title: AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium Volume: 2011 ISSN: 1942-597X ISO Abbreviation: AMIA Annu Symp Proc Publication Date: 2011 |
Date Detail:
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Created Date: 2011-12-23 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101209213 Medline TA: AMIA Annu Symp Proc Country: United States |
Other Details:
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Languages: eng Pagination: 979-86 Citation Subset: IM |
Affiliation:
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Institute for Health Informatics; |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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