Assertion modeling and its role in clinical phenotype identification

Authors:
Cosmin Adrian Bejan;Lucy Vanderwende;Fei Xia;Meliha Yetisgen-Yildiz
Affiliations:
Biomedical and Health Informatics, University of Washington, Seattle, WA 98195, United States;Microsoft Research, Microsoft, Redmond, WA 98052, United States and Biomedical and Health Informatics, University of Washington, Seattle, WA 98195, United States;Department of Linguistics, University of Washington, Seattle, WA 98195, United States and Biomedical and Health Informatics, University of Washington, Seattle, WA 98195, United States;Biomedical and Health Informatics, University of Washington, Seattle, WA 98195, United States and Department of Linguistics, University of Washington, Seattle, WA 98195, United States
Venue:
Journal of Biomedical Informatics
Year:
2013

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Abstract

This paper describes an approach to assertion classification and an empirical study on the impact this task has on phenotype identification, a real world application in the clinical domain. The task of assertion classification is to assign to each medical concept mentioned in a clinical report (e.g., pneumonia, chest pain) a specific assertion category (e.g., present, absent, and possible). To improve the classification of medical assertions, we propose several new features that capture the semantic properties of special cue words highly indicative of a specific assertion category. The results obtained outperform the current state-of-the-art results for this task. Furthermore, we confirm the intuition that assertion classification contributes in significantly improving the results of phenotype identification from free-text clinical records.