title: Semantic processing to identify adverse drug event information from black box warnings.
abstract: Adverse drug events account for two million combined injuries, hospitalizations, or deaths each year. Furthermore, there are few comprehensive, up-to-date, and free sources of drug information. Clinical decision support systems may significantly mitigate the number of adverse drug events. However, these systems depend on up-to-date, comprehensive, and codified data to serve as input. The DailyMed website, a resource managed by the FDA and NLM, contains all currently approved drugs. We used a semantic natural language processing approach that successfully extracted information for adverse drug events, at-risk conditions, and susceptible populations from black box warning labels on this site. The precision, recall, and F-score were, 94%, 52%, 0.67 for adverse drug events; 80%, 53%, and 0.64 for conditions; and 95%, 44%, 0.61 for populations. Overall performance was 90% precision, 51% recall, and 0.65 F-Score. Information extracted can be stored in a structured format and may 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; D005240:Feasibility Studies; D006801:Humans; D020407:Internet; D009323:Natural Language Processing; D055553:Prescription Drugs / Q000009:adverse effects*; D012660:Semantics; D014481:United States; D014486:United States Food and Drug Administration
chemical_list: D055553:Prescription Drugs