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- W2013770071 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." @default.
- W2013770071 created "2016-06-24" @default.
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- W2013770071 date "2014-02-19" @default.
- W2013770071 modified "2023-10-18" @default.
- W2013770071 title "Automated Detection of Off-Label Drug Use" @default.
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- W2013770071 doi "https://doi.org/10.1371/journal.pone.0089324" @default.
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