Hawkes Process Modeling of Adverse Drug Reactions with Longitudinal Observational Data

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v68-bao17a, 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}, pages = {177--190}, year = {2017}, editor = {Doshi-Velez, Finale and Fackler, Jim and Kale, David and Ranganath, Rajesh and Wallace, Byron and Wiens, Jenna}, volume = {68}, series = {Proceedings of Machine Learning Research}, month = {18--19 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v68/bao17a/bao17a.pdf}, url = {https://proceedings.mlr.press/v68/bao17a.html}, 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.} }
Endnote
%0 Conference Paper %T Hawkes Process Modeling of Adverse Drug Reactions with Longitudinal Observational Data %A Yujia Bao %A Zhaobin Kuang %A Peggy Peissig %A David Page %A Rebecca Willett %B Proceedings of the 2nd Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2017 %E Finale Doshi-Velez %E Jim Fackler %E David Kale %E Rajesh Ranganath %E Byron Wallace %E Jenna Wiens %F pmlr-v68-bao17a %I PMLR %P 177--190 %U https://proceedings.mlr.press/v68/bao17a.html %V 68 %X 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.
APA
Bao, Y., Kuang, Z., Peissig, P., Page, D. & Willett, R.. (2017). Hawkes Process Modeling of Adverse Drug Reactions with Longitudinal Observational Data. Proceedings of the 2nd Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 68:177-190 Available from https://proceedings.mlr.press/v68/bao17a.html.

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