title: Biclustering of adverse drug events in the FDA's spontaneous reporting system.
abstract: In this article, we present a new pharmacovigilance data mining technique based on the biclustering paradigm, which is designed to identify drug groups that share a common set of adverse events (AEs) in the spontaneous reporting system (SRS) of the US Food and Drug Administration (FDA). A taxonomy of biclusters is developed, revealing that a significant number of bona fide adverse drug event (ADE) biclusters have been identified. Statistical tests indicate that it is extremely unlikely that the bicluster structures thus discovered, as well as their content, could have arisen by mere chance. Some of the biclusters classified as indeterminate provide support for previously unrecognized and potentially novel ADEs. In addition, we demonstrate the potential importance of the proposed methodology in several important aspects of pharmacovigilance such as providing insight into the etiology of ADEs, facilitating the identification of novel ADEs, suggesting methods and a rationale for aggregating terminologies, highlighting areas of focus, and providing an exploratory tool for data mining.
authors.affiliation: Department of Biomedical Informatics, Columbia University Medical Center, New York, New York, USA. rave.harpaz@dbmi.columbia.edu
authors.forename: R
authors.initials: R
authors.lastname: Harpaz
authors.forename: H
authors.initials: H
authors.lastname: Perez
authors.forename: H S
authors.initials: HS
authors.lastname: Chase
authors.forename: R
authors.initials: R
authors.lastname: Rabadan
authors.forename: G
authors.initials: G
authors.lastname: Hripcsak
authors.forename: C
authors.initials: C
authors.lastname: Friedman
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D016000:Cluster Analysis; D003625:Data Collection; D057225:Data Mining; D006801:Humans; D014481:United States; D014486:United States Food and Drug Administration