Document Detail


Throw the bath water out, keep the baby: keeping medically-relevant terms for text mining.
MedLine Citation:
PMID:  21346996     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
The purpose of this research is to answer the question, can medically-relevant terms be extracted from text notes and text mined for the purpose of classification and obtain equal or better results than text mining the original note? A novel method is used to extract medically-relevant terms for the purpose of text mining. A dataset of 5,009 EMR text notes (1,151 related to falls) was obtained from a Veterans Administration Medical Center. The dataset was processed with a natural language processing (NLP) application which extracted concepts based on SNOMED-CT terms from the Unified Medical Language System (UMLS) Metathesaurus. SAS Enterprise Miner was used to text mine both the set of complete text notes and the set represented by the extracted concepts. Logistic regression models were built from the results, with the extracted concept model performing slightly better than the complete note model.
Authors:
Jay Jarman; Donald J Berndt
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Publication Detail:
Type:  Journal Article     Date:  2010-11-13
Journal Detail:
Title:  AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium     Volume:  2010     ISSN:  1942-597X     ISO Abbreviation:  AMIA Annu Symp Proc     Publication Date:  2010  
Date Detail:
Created Date:  2011-02-24     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101209213     Medline TA:  AMIA Annu Symp Proc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  336-40     Citation Subset:  IM    
Affiliation:
James A. Haley VAMC, Tampa, FL;
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