Document Detail

Unsupervised method for extracting machine understandable medical knowledge from a large free text collection.
MedLine Citation:
PMID:  20351945     Owner:  NLM     Status:  MEDLINE    
Definitions of medical concepts (e.g diseases, drugs) are essential background knowledge for researchers, clinicians and health care consumers. However, the rapid growth of biomedical research requires that such knowledge continually needs updating. To address this problem, we have developed an unsupervised pattern learning approach that extracts disease and drug definitions from automatically structured randomized clinical trial (RCT) abstracts. In addition, each extracted definition is semantically classified without relying on external medical knowledge. When used to identify definitions from 100 manually annotated RCT abstracts, our medical definition knowledge base has precision of 0.97, recall of 0.93, F1 of 0.94 and semantic classification accuracy of 0.96.
Rong Xu; Amar K Das; Alan M Garber
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2009-11-14
Journal Detail:
Title:  AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium     Volume:  2009     ISSN:  1942-597X     ISO Abbreviation:  AMIA Annu Symp Proc     Publication Date:  2009  
Date Detail:
Created Date:  2010-03-30     Completed Date:  2011-04-11     Revised Date:  2013-05-29    
Medline Journal Info:
Nlm Unique ID:  101209213     Medline TA:  AMIA Annu Symp Proc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  709-13     Citation Subset:  IM    
Center for Biomedical Informatics Research.
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MeSH Terms
Abstracting and Indexing as Topic*
Information Storage and Retrieval / methods*
Natural Language Processing*
Pattern Recognition, Automated*
Randomized Controlled Trials as Topic

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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