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- Title
Natural language processing framework to assess clinical conditions.
- Authors
Ware, Henry; Mullett, Charles J; Jagannathan, V
- Abstract
OBJECTIVE The authors developed a natural language processing (NLP) framework that could be used to extract clinical findings and diagnoses from dictated physician documentation. DESIGN De-identified documentation was made available by i2b2 Bio-informatics research group as a part of their NLP challenge focusing on obesity and its co-morbidities. The authors describe their approach, which used a combination of concept detection, context validation, and the application of a variety of rules to conclude patient diagnoses. RESULTS The framework was successful at correctly identifying diagnoses as judged by NLP challenge organizers when compared with a gold standard of physician annotations. The authors overall kappa values for agreement with the gold standard were 0.92 for explicit textual results and 0.91 for intuited results. The NLP framework compared favorably with those of the other entrants, placing third in textual results and fourth in intuited results in the i2b2 competition. CONCLUSIONS The framework and approach used to detect clinical conditions was reasonably successful at extracting 16 diagnoses related to obesity. The system and methodology merits further development, targeting clinically useful applications.
- Publication
Journal of the American Medical Informatics Association : JAMIA, 2009, Vol 16, Issue 4, p585
- ISSN
1067-5027
- Publication type
Journal Article
- DOI
10.1197/jamia.M3091