title: Automated detection of off-label drug use.
abstract: Off-label drug use, defined as use of a drug in a manner that deviates from its approved use defined by the drug's FDA label, is problematic because such uses have not been evaluated for safety and efficacy. Studies estimate that 21% of prescriptions are off-label, and only 27% of those have evidence of safety and efficacy. We describe a data-mining approach for systematically identifying off-label usages using features derived from free text clinical notes and features extracted from two databases on known usage (Medi-Span and DrugBank). We trained a highly accurate predictive model that detects novel off-label uses among 1,602 unique drugs and 1,472 unique indications. We validated 403 predicted uses across independent data sources. Finally, we prioritize well-supported novel usages for further investigation on the basis of drug safety and cost.
authors.affiliation: Program In Biomedical Informatics, Stanford University, Stanford, California, United States of America.
authors.forename: Kenneth
authors.initials: K
authors.lastname: Jung
authors.affiliation: Center for Biomedical Informatics Research, Stanford University, Stanford, California, United States of America.
authors.forename: Paea
authors.initials: P
authors.lastname: LePendu
authors.affiliation: Center for Biomedical Informatics Research, Stanford University, Stanford, California, United States of America.
authors.forename: William S
authors.initials: WS
authors.lastname: Chen
authors.affiliation: Program In Biomedical Informatics, Stanford University, Stanford, California, United States of America.
authors.forename: Srinivasan V
authors.initials: SV
authors.lastname: Iyer
authors.affiliation: Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.
authors.forename: Ben
authors.initials: B
authors.lastname: Readhead
authors.affiliation: Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.
authors.forename: Joel T
authors.initials: JT
authors.lastname: Dudley
authors.affiliation: Center for Biomedical Informatics Research, Stanford University, Stanford, California, United States of America.
authors.forename: Nigam H
authors.initials: NH
authors.lastname: Shah
mesh_terms: D000465:Algorithms; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D008962:Models, Theoretical; D056687:Off-Label Use / Q000706:statistics & numerical data*; D010363:Pattern Recognition, Automated / Q000379:methods*