Automated detection of off-label drug use.
Automated detection of off-label drug use.
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DOI：
10.1371/journal.pone.0089324
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Shah NH
中科院分区：
文献类型：
--
作者：
Jung K;LePendu P;Chen WS;Iyer SV;Readhead B;Dudley JT;Shah NH
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.
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影响因子：
6.7
作者：
Dal Pan, G. J.
通讯作者：
Dal Pan, G. J.
影响因子：
9.9
作者：
Kuhn M;Campillos M;Letunic I;Jensen LJ;Bork P
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Lash, AE
DOI：
10.1136/amiajnl-2012-001083
发表时间：
2013-05-01
影响因子：
6.4
作者：
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通讯作者：
Fieschi, Marius
影响因子：
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通讯作者：
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