Several prominent public health incidents [29] that occurred at the beginning of this century due to adverse drug events (ADEs) have raised international awareness of governments and industries about pharmacovigilance (PhV) [6, 7] , the science and activities to monitor and prevent adverse events caused by pharmaceutical products a er they are introduced to the market. A major data source for PhV is large-scale longitudinal observational databases (LODs) [6] such as electronic health records (EHRs) and medical insurance claim databases. Inspired by the Multiple Self-Controlled Case Series (MSCCS) model [27] , arguably the leading method for ADE discovery from LODs, we propose baseline regularization, a regularized generalized linear model that leverages the diverse health pro les available in LODs across di erent individuals at di erent times. We apply the proposed method as well as MSCCS to the Marsh eld Clinic EHR. Experimental results suggest that incorporating the heterogeneity among di erent patients and di erent times help to improve the performance in identifying benchmark ADEs from the Observational Medical Outcomes Partnership ground truth [26] .
Moreover, REF propose baseline regularization to leverage the diverse health profiles for adverse drug events.
Pharmacovigilance via Baseline Regularization with Large-Scale Longitudinal Observational Data
KDD '17
Computer Science Medicine