Document Detail


Evaluating the UMLS as a source of lexical knowledge for medical language processing.
MedLine Citation:
PMID:  11825178     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Medical language processing (MLP) systems rely on specialized lexicons in order to recognize, classify, and normalize medical terminology, and the performance of an MLP system is dependent on the coverage and quality of such lexicons. However, the acquisition of lexical knowledge is expensive and time-consuming. The UMLS is a comprehensive resource that can be used to acquire lexical knowledge needed for medical language processing. This paper describes methods that use these resources to automatically create lexical entries and generate two lexicons. The first lexicon was created primarily using the UMLS, whereas the second was created by supplementing the lexicon of an existing MLP system called MedLEE with entries based on the UMLS. We subsequently carried out a study, which is the primary focus of this paper, using MedLEE with each of the two lexicons and also the current MedLEE lexicon to measure performance. Overall accuracy, sensitivity, and specificity using the lexicon primarily based on the UMLS were.86,.60, and.96 respectively. Those measures using the MedLEE lexicon alone were.93,.81, and.93, which was significantly better except for specificity; performance using the supplemental lexicon was exactly the same as performance using solely the MedLEE lexicon.
Authors:
C Friedman; H Liu; L Shagina; S Johnson; G Hripcsak
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Publication Detail:
Type:  Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, P.H.S.    
Journal Detail:
Title:  Proceedings / AMIA ... Annual Symposium. AMIA Symposium     Volume:  -     ISSN:  1531-605X     ISO Abbreviation:  Proc AMIA Symp     Publication Date:  2001  
Date Detail:
Created Date:  2002-02-04     Completed Date:  2002-05-24     Revised Date:  2009-11-18    
Medline Journal Info:
Nlm Unique ID:  100883449     Medline TA:  Proc AMIA Symp     Country:  United States    
Other Details:
Languages:  eng     Pagination:  189-93     Citation Subset:  IM    
Affiliation:
Computer Science Department, Queens College, CUNY, USA.
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MeSH Terms
Descriptor/Qualifier:
Natural Language Processing*
Unified Medical Language System*
Vocabulary, Controlled*
Grant Support
ID/Acronym/Agency:
LM0624/LM/NLM NIH HHS
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