A time-indexed reference standard of adverse drug reactions

被引:0
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作者
Rave Harpaz
David Odgers
Greg Gaskin
William DuMouchel
Rainer Winnenburg
Olivier Bodenreider
Anna Ripple
Ana Szarfman
Alfred Sorbello
Eric Horvitz
Ryen W. White
Nigam H. Shah
机构
[1] Center for Biomedical Informatics Research,
[2] Stanford University,undefined
[3] Oracle Health Sciences,undefined
[4] National Library of Medicine,undefined
[5] NIH,undefined
[6] U.S. FDA,undefined
[7] Microsoft Research,undefined
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Undetected adverse drug reactions (ADRs) pose a major burden on the health system. Data mining methodologies designed to identify signals of novel ADRs are of deep importance for drug safety surveillance. The development and evaluation of these methodologies requires proper reference benchmarks. While progress has recently been made in developing such benchmarks, our understanding of the performance characteristics of the data mining methodologies is limited because existing benchmarks do not support prospective performance evaluations. We address this shortcoming by providing a reference standard to support prospective performance evaluations. The reference standard was systematically curated from drug labeling revisions, such as new warnings, which were issued and communicated by the US Food and Drug Administration in 2013. The reference standard includes 62 positive test cases and 75 negative controls, and covers 44 drugs and 38 events. We provide usage guidance and empirical support for the reference standard by applying it to analyze two data sources commonly mined for drug safety surveillance.
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