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J Am Med Inform Assoc. 2009 Jul-Aug;16(4):571-5. doi: 10.1197/jamia.M3083. Epub 2009 Apr 23.

Description of a rule-based system for the i2b2 challenge in natural language processing for clinical data.

Author information

  • 1Lockheed Martin, Inc., Valley Forge, Philadelphia, PA, USA. lois.childs@lmco.com

Abstract

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.

PMID:
19390103
[PubMed - indexed for MEDLINE]
PMCID:
PMC2705261
Free PMC Article

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