Hawkes Process Modeling of Adverse Drug Reactions with Longitudinal Observational Data

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Yujia Bao, Zhaobin Kuang, Peggy Peissig, David Page, Rebecca Willett ;
Proceedings of the 2nd Machine Learning for Healthcare Conference, PMLR 68:177-190, 2017.

Abstract

Adverse drug reaction (ADR) discovery is the task of identifying unexpected and negative events caused by pharmaceutical products. This paper describes a log-linear Hawkes process model for ADR discovery from longitudinal observational data such as electronic health records (EHRs). The proposed method leverages the irregular time-stamped events in EHRs to represent the time-varying effect of various drugs on the occurrence rate of adverse events. Experimental results on a large-scale cohort of real-world EHRs demonstrate that the proposed method outperforms a leading approach, multiple self-controlled case series (Simpson et al., 2013), in identifying benchmark ADRs defined by the Observational Medical Outcomes Partnership.

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