title: Semantic processing to identify adverse drug event information from black box warnings.
abstract: We utilized a semantic natural language processing approach to extract adverse drug event information from FDA black box warnings. Overall performance was 90% precision, 51% recall, and 0.65 F-Score. Information extracted can be stored in a structured format and may be useful to support clinical decision support systems.
authors.affiliation: Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD.
authors.forename: Adam
authors.initials: A
authors.lastname: Culbertson
authors.affiliation: Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD.
authors.forename: Marcelo
authors.initials: M
authors.lastname: Fiszman
authors.affiliation: Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD.
authors.forename: Dongwook
authors.initials: D
authors.lastname: Shin
authors.affiliation: Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD.
authors.forename: Thomas C
authors.initials: TC
authors.lastname: Rindflesch
mesh_terms: D004348:Drug Labeling; D064420:Drug-Related Side Effects and Adverse Reactions; D009323:Natural Language Processing; D012660:Semantics