Automated detection of off-label drug use

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

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

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.

Original languageEnglish (US)
Article numbere89324
JournalPloS one
Volume9
Issue number2
DOIs
StatePublished - Feb 19 2014

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

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