Document Detail

Description of a rule-based system for the i2b2 challenge in natural language processing for clinical data.
MedLine Citation:
PMID:  19390103     Owner:  NLM     Status:  MEDLINE    
The Obesity Challenge, sponsored by Informatics for Integrating Biology and the Bedside (i2b2), a National Center for Biomedical Computing, asked participants to build software systems that could "read" a patient's clinical discharge summary and replicate the judgments of physicians in evaluating presence or absence of obesity and 15 comorbidities. The authors describe their methodology and discuss the results of applying Lockheed Martin's rule-based natural language processing (NLP) capability, ClinREAD. We tailored ClinREAD with medical domain expertise to create assigned default judgments based on the most probable results as defined in the ground truth. It then used rules to collect evidence similar to the evidence that the human judges likely relied upon, and applied a logic module to weigh the strength of all evidence collected to arrive at final judgments. The Challenge results suggest that rule-based systems guided by human medical expertise are capable of solving complex problems in machine processing of medical text.
Lois C Childs; Robert Enelow; Lone Simonsen; Norris H Heintzelman; Kimberly M Kowalski; Robert J Taylor
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Publication Detail:
Type:  Journal Article     Date:  2009-04-23
Journal Detail:
Title:  Journal of the American Medical Informatics Association : JAMIA     Volume:  16     ISSN:  1067-5027     ISO Abbreviation:  J Am Med Inform Assoc     Publication Date:    2009 Jul-Aug
Date Detail:
Created Date:  2009-07-03     Completed Date:  2009-09-03     Revised Date:  2013-06-02    
Medline Journal Info:
Nlm Unique ID:  9430800     Medline TA:  J Am Med Inform Assoc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  571-5     Citation Subset:  IM    
Lockheed Martin, Inc., Valley Forge, Philadelphia, PA, USA.
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MeSH Terms
Expert Systems
Information Storage and Retrieval / methods
Knowledge Bases
Medical Records Systems, Computerized*
Natural Language Processing*
Pattern Recognition, Automated*

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