Document Detail


Tracking medical students' clinical experiences using natural language processing.
MedLine Citation:
PMID:  19236956     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Graduate medical students must demonstrate competency in clinical skills. Current tracking methods rely either on manual efforts or on simple electronic entry to record clinical experience. We evaluated automated methods to locate 10 institution-defined core clinical problems from three medical students' clinical notes (n=290). Each note was processed with section header identification algorithms and the KnowledgeMap concept identifier to locate Unified Medical Language System (UMLS) concepts. The best performing automated search strategies accurately classified documents containing primary discussions to the core clinical problems with area under receiver operator characteristic curve of 0.90-0.94. Recall and precision for UMLS concept identification was 0.91 and 0.92, respectively. Of the individual note section, concepts found within the chief complaint, history of present illness, and assessment and plan were the strongest predictors of relevance. This automated method of tracking can provide detailed, pertinent reports of clinical experience that does not require additional work from medical trainees. The coupling of section header identification and concept identification holds promise for other natural language processing tasks, such as clinical research or phenotype identification.
Authors:
Joshua C Denny; Lisa Bastarache; Elizabeth Ann Sastre; Anderson Spickard
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural     Date:  2009-02-21
Journal Detail:
Title:  Journal of biomedical informatics     Volume:  42     ISSN:  1532-0480     ISO Abbreviation:  J Biomed Inform     Publication Date:  2009 Oct 
Date Detail:
Created Date:  2009-10-05     Completed Date:  2010-01-21     Revised Date:  2013-08-01    
Medline Journal Info:
Nlm Unique ID:  100970413     Medline TA:  J Biomed Inform     Country:  United States    
Other Details:
Languages:  eng     Pagination:  781-9     Citation Subset:  IM    
Affiliation:
Department of Biomedical Informatics, Vanderbilt University Medical Center, Eskind Biomedical Library, Room 442, 2209 Garland Ave., Nashville, TN 37232, USA. josh.denny@vanderbilt.edu
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MeSH Terms
Descriptor/Qualifier:
Clinical Competence*
Data Interpretation, Statistical
Education, Medical, Graduate*
Humans
Medical Informatics / methods*
Natural Language Processing*
Students, Medical
Unified Medical Language System
User-Computer Interface
Grant Support
ID/Acronym/Agency:
R01 LM009965/LM/NLM NIH HHS; T15 LM007450/LM/NLM NIH HHS

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


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