Document Detail


A knowledge model for the interpretation and visualization of NLP-parsed discharged summaries.
MedLine Citation:
PMID:  11825207     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
At our institution, a Natural Language Processing (NLP) tool called MedLEE is used on a daily basis to parse medical texts including complete discharge summaries. MedLEE transforms written text into a generic structured format, which preserves the richness of the underlying natural language expressions by the use of concept modifiers (like change, certainty, degree and status). As a tradeoff, extraction of application-specific medical information is difficult without a clear understanding of how these modifiers combine. We report on a knowledge model for MedLEE modifiers that is helpful for a high level interpretation of NLP data and is used for the generation of two distinct views on NLP-parsed discharge summaries: A physician view offering a condensed overview of the severity of patient problems and a data mining view featuring binary problem states useful for machine learning.
Authors:
M Krauthammer; G Hripcsak
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Publication Detail:
Type:  Evaluation Studies; Journal Article; 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:  339-43     Citation Subset:  IM    
Affiliation:
Medical Informatics, Columbia University, New York, NY, USA.
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MeSH Terms
Descriptor/Qualifier:
Artificial Intelligence
Humans
Medical Records Systems, Computerized
Natural Language Processing*
Patient Discharge*
Severity of Illness Index
User-Computer Interface
Grant Support
ID/Acronym/Agency:
R01-LM06274/LM/NLM NIH HHS; R01-LM06910/LM/NLM NIH HHS
Comments/Corrections

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