AMIA Annu Symp Proc - Statistical Mining of Potential Drug Interaction Adverse Effects in FDA's Spontaneous Reporting System.

Tópicos

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{ compound(1573) activ(1297) structur(1058) }
{ model(3404) distribut(989) bayesian(671) }
{ data(1737) use(1416) pattern(1282) }
{ bind(1733) structur(1185) ligand(1036) }
{ studi(2440) review(1878) systemat(933) }
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{ data(3963) clinic(1234) research(1004) }
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{ can(774) often(719) complex(702) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Many adverse drug effects (ADEs) can be attributed to drug interactions. Spontaneous reporting systems (SRS) provide a rich opportunity to detect novel post-marketed drug interaction adverse effects (DIAEs), as they include populations not well represented in clinical trials. However, their identification in SRS is nontrivial. Most existing research have addressed the statistical issues used to test or verify DIAEs, but not their identification as part of a systematic large scale database-wide mining process as discussed in this work. This paper examines the application of a highly optimized and tailored implementation of the Apriori algorithm, as well as methods addressing data quality issues, to the identification of DIAEs in FDAs SRS.

Resumo Limpo

mani advers drug effect ade can attribut drug interact spontan report system srs provid rich opportun detect novel postmarket drug interact advers effect diae includ popul well repres clinic trial howev identif srs nontrivi exist research address statist issu use test verifi diae identif part systemat larg scale databasewid mine process discuss work paper examin applic high optim tailor implement apriori algorithm well method address data qualiti issu identif diae fdas srs

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