Journal Article

Automated Detection of Off-Label Drug Use

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

2014

Overview

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.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Algorithms / Automation / Clinical medicine / Computer Science / Data mining / Data Mining - methods / Data processing / Databases, Factual / Disease / Drugs / Electronic health records / Feature extraction / Gene expression / Informatics / Labeling / Lupus / Medical records / Medicine / Models, Theoretical / Natural language / Natural language processing / Off-Label Use - statistics & numerical data / Patient safety / Pattern Recognition, Automated - methods / Pharmacovigilance / Prediction models / Researchers / Safety / Surveillance