Hawkes Process Modeling of Adverse Drug Reactions with Longitudinal Observational Data
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.
Cite
Text
Bao et al. "Hawkes Process Modeling of Adverse Drug Reactions with Longitudinal Observational Data." Proceedings of the 2nd Machine Learning for Healthcare Conference, 2017.Markdown
[Bao et al. "Hawkes Process Modeling of Adverse Drug Reactions with Longitudinal Observational Data." Proceedings of the 2nd Machine Learning for Healthcare Conference, 2017.](https://mlanthology.org/mlhc/2017/bao2017mlhc-hawkes/)BibTeX
@inproceedings{bao2017mlhc-hawkes,
title = {{Hawkes Process Modeling of Adverse Drug Reactions with Longitudinal Observational Data}},
author = {Bao, Yujia and Kuang, Zhaobin and Peissig, Peggy and Page, David and Willett, Rebecca},
booktitle = {Proceedings of the 2nd Machine Learning for Healthcare Conference},
year = {2017},
pages = {177-190},
volume = {68},
url = {https://mlanthology.org/mlhc/2017/bao2017mlhc-hawkes/}
}