A Knowledge Intensive Approach to Mapping Clinical Narrative to LOINC.
Many natural language processing systems are being applied to clinical text, yet clinically useful results are obtained only by honing a system to a particular context. We suggest that concentration on the information needed for this processing is crucial and present a knowledge intensive methodology for mapping clinical text to LOINC. The system takes published case reports as input and maps vital signs and body measurements and reports of diagnostic procedures to fully specified LOINC codes. Three kinds of knowledge are exploited: textual, ontological, and pragmatic (including information about physiology and the clinical process). Evaluation on 4809 sentences yielded precision of 89% and recall of 93% (F-score 0.91). Our method could form the basis for a system to provide semi-automated help to human coders.
AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium. 2010 ;2010():227-31.
Authors: Marcelo Fiszman, Dongwook Shin, Charles A Sneiderman, Honglan Jin, Thomas C Rindflesch
PMID 21346974, PMC3041410