Empirical Bayesian data mining for discovering patterns in post-marketing drug safety

Because of practical limits in characterizing the safety profiles of therapeutic products prior to marketing, manufacturers and regulatory agencies perform post-marketing surveillance based on the collection of adverse reaction reports ("pharmacovigilance"). The resulting databases, while rich in real-world information, are notoriously difficult to analyze using traditional techniques. Each report may involve multiple medicines, symptoms, and demographic factors, and there is no easily linked information on drug exposure in the reporting population. KDD techniques, such as association finding, are well-matched to the problem, but are difficult for medical staff to apply and interpret. To deploy KDD effectively for pharmacovigilance, Lincoln Technologies and GlaxoSmithKline collaborated to create a webbased safety data mining web environment. The analytical core is a high-performance implementation of the MGPS (Multi-Item Gamma Poisson Shrinker) algorithm described previously...

• Type: Conference
• Year: 2003
• Where: KDD
• Authors: David M. Fram, June S. Almenoff, William DuMouchel
• Keywords: Adverse Event Data, Data Mining, KDD 2003, Multi-Item Gamma Poisson, Safety Data Mining