title: Assertion modeling and its role in clinical phenotype identification.
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.
authors.affiliation: Biomedical and Health Informatics, University of Washington, Seattle, WA 98195-7240, United States. bejan@u.washington.edu
authors.forename: Cosmin Adrian
authors.initials: CA
authors.lastname: Bejan
authors.forename: Lucy
authors.initials: L
authors.lastname: Vanderwende
authors.forename: Fei
authors.initials: F
authors.lastname: Xia
authors.forename: Meliha
authors.initials: M
authors.lastname: Yetisgen-Yildiz
mesh_terms: D006801:Humans; D008962:Models, Theoretical; D010641:Phenotype; D011014:Pneumonia / Q000503:physiopathology*