title: Statistical Mining of Potential Drug Interaction Adverse Effects in FDA's Spontaneous Reporting System.
abstract: 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.
authors.affiliation: Dept of Biomedical Informatics, Columbia University, New York, NY.
authors.forename: Rave
authors.initials: R
authors.lastname: Harpaz
authors.forename: Krystl
authors.initials: K
authors.lastname: Haerian
authors.forename: Herbert S
authors.initials: HS
authors.lastname: Chase
authors.forename: Carol
authors.initials: C
authors.lastname: Friedman
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems; D016208:Databases, Factual; D004347:Drug Interactions; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D014486:United States Food and Drug Administration