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.