Automated Detection of Off-Label Drug Use

Kenneth Jung, Paea LePendu, William S Chen, Srinivasan V Iyer, Ben Readhead, Joel T Dudley and Nigam H Shah

PLOS ONE, 2014, vol. 9, issue 2, 1-9

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.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0089324

DOI: 10.1371/journal.pone.0089324

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