Several prominent public health incidents  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 after they are introduced to the market. A major data source for PhV is large-scale longitudinal observational databases (LODs)  such as electronic health records (EHRs) and medical insurance claim databases. Inspired by the Multiple Self-Controlled Case Series (MSCCS) model , arguably the leading method for ADE discovery from LODs, we propose baseline regularization, a regularized generalized linear model that leverages the diverse health profiles available in LODs across different individuals at different times. We apply the proposed method as well as MSCCS to the Marshfield Clinic EHR. Experimental results suggest that incorporatingthe heterogeneity among different patients and different times help to improve the performance in identifying benchmark ADEs from the Observational Medical Outcomes Partnership ground truth .
|Original language||English (US)|
|Title of host publication||KDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining|
|Publisher||Association for Computing Machinery|
|Number of pages||10|
|State||Published - Aug 13 2017|
|Event||23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017 - Halifax, Canada|
Duration: Aug 13 2017 → Aug 17 2017
|Name||Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining|
|Other||23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017|
|Period||8/13/17 → 8/17/17|
Bibliographical noteFunding Information:
Œe authors would like to gratefully acknowledge the NIH BD2K Initiative grant U54 AI117924, the NIGMS grant 2RO1 GM097618, and the P2020—Norte2020 grant NanoSTIMA/NORTE-01-0145-FEDER-00001. Œe authors would like to thank Dr. David Madigan from Columbia University for his constructive comment and advice. Œe authors would like to thank the anonymous reviewers for their reviews and feedback. Zhanrong Du and Sinong Geng from the University of Wisconsin-Madison and Dr. Shijia Wang from Harvard Medical School participated in helpful discussion. Œey are also gratefully acknowledged.
© 2017 Copyright held by the owner/author(s).
- Adverse drug event discovery
- Baseline regularization
- Electronic health records
- Longitudinal data