document: 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 by DuMouchel and Pregibon , with several significant extensions and enhancements . The environment offers an interface for specifying data mining runs , a batch execution facility , tabular and graphical methods for exploring associations , and drilldown to case details . Substantial work was involved in preparing the raw adverse event data for mining , including harmonization of drug names and removal of duplicate reports . The environment can be used to explore both drug-event and multi-way associations ( interactions , syndromes ) . It has been used to study age\/gender effects , to predict the safety profiles of proposed combination drugs , and to separate contributions of individual drugs to safety problems in polytherapy situations .
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