Medical College of Wisconsin
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Mining patterns of adverse events using aggregated clinical trial results. AMIA Jt Summits Transl Sci Proc 2013;2013:112-6



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Adverse event reports contain the most important metrics for evaluating the hazards, harms, and risks of a clinical intervention. In this paper, we present an exploratory study of discovering internal association patterns between adverse events. By taking advantage of the published trials reports on, we developed an automatic pipeline to create a Clinical Trial Adverse Event Database (cTAED), which currently stores 4,317 clinical trial reports and 11,362 adverse events. The association mining algorithm FP-Tree was applied to the cTAED data to discover patterns between adverse events. We extracted 29,546 patterns and further examined association patterns related to patients' deaths. The mined results indicate the existence of strong internal association patterns between adverse events. The evaluation results show that the p-value of confidence is smaller than 0.001, which indicates that our method mined association patterns with significantly more confidence than randomly-associated adverse events.

Author List

Luo Z, Zhang GQ, Xu R


Jake Luo Ph.D. Associate Professor; Director, Center for Biomedical Data and Language Processing (BioDLP) in the Health Informatics & Administration department at University of Wisconsin - Milwaukee