title: Data mining for signal detection of adverse event safety data.
abstract: The Adverse Event Reporting System (AERS) is the primary database designed to support the Food and Drug Administration (FDA) postmarketing safety surveillance program for all approved drugs and therapeutic biologic products. Most current disproportionality analysis focuses on the detection of potential adverse events (AE) involving a single drug and a single AE only. In this paper, we present a data mining biclustering technique based on the singular value decomposition to extract local regions of association for a safety study. The analysis consists of collection of biclusters, each representing an association between a set of drugs with the corresponding set of adverse events. Significance of each bicluster can be tested using disproportionality analysis. Individual drug-event combination can be further tested. A safety data set consisting of 193 drugs with 8453 adverse events is analyzed as an illustration.
authors: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas 72079, USA.
authors: Hung-Chia
authors: HC
authors: Chen
authors: Yi
authors: Y
authors: Tsong
authors: James J
authors: JJ
authors: Chen
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D017603:Signal Detection, Psychological