Pharmacovigilance via baseline regularization with large-scale longitudinal observational data

Zhaobin Kuang, Peggy Peissig, Vítor Santos Costa, Richard Maclin, David Page

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

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 after 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 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 [26].

Original languageEnglish (US)
Title of host publicationKDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages1537-1546
Number of pages10
ISBN (Electronic)9781450348874
DOIs
StatePublished - Aug 13 2017
Externally publishedYes
Event23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017 - Halifax, Canada
Duration: Aug 13 2017Aug 17 2017

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
VolumePart F129685

Other

Other23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017
CountryCanada
CityHalifax
Period8/13/178/17/17

Bibliographical note

Funding 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.

Publisher Copyright:
© 2017 Copyright held by the owner/author(s).

Keywords

  • Adverse drug event discovery
  • Baseline regularization
  • Electronic health records
  • Longitudinal data
  • Pharmacovigilance

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