Predicting Smoking Events with a Time-Varying Semi-Parametric Hawkes Process Model

Matthew Engelhard, Hongteng Xu, Lawrence Carin, Jason A. Oliver, Matthew Hallyburton, F. Joseph McClernon
Proceedings of the 3rd Machine Learning for Healthcare Conference, PMLR 85:312-331, 2018.

Abstract

Health risks from cigarette smoking – the leading cause of preventable death in the United States – can be substantially reduced by quitting. Although most smokers are motivated to quit, the majority of quit attempts fail. A number of studies have explored the role of self-reported symptoms, physiologic measurements, and environmental context on smoking risk, but less work has focused on the temporal dynamics of smoking events, including daily patterns and related nicotine effects. In this work, we examine these dynamics and improve risk prediction by modeling smoking as a self-triggering process, in which previous smoking events modify current risk. Specifically, we fit smoking events self-reported by 42 smokers to a time-varying semi-parametric Hawkes process (TV-SPHP) developed for this purpose. Results show that the TV-SPHP achieves superior prediction performance compared to related and existing models, with incorporation of time-varying predictors having greatest benefit over longer prediction windows. Moreover, the impact function illustrates previously unknown temporal dynamics of smoking, with possible connections to nicotine metabolism to be explored in future work through a randomized study design. By more effectively predicting smoking events and exploring a self-triggering component of smoking risk, this work supports development of novel or improved cessation interventions that aim to reduce death from smoking.

Cite this Paper

BibTeX
@InProceedings{pmlr-v85-engelhard18a, title = {Predicting Smoking Events with a Time-Varying Semi-Parametric Hawkes Process Model}, author = {Engelhard, Matthew and Xu, Hongteng and Carin, Lawrence and Oliver, Jason A. and Hallyburton, Matthew and McClernon, F. Joseph}, booktitle = {Proceedings of the 3rd Machine Learning for Healthcare Conference}, pages = {312--331}, year = {2018}, editor = {Doshi-Velez, Finale and Fackler, Jim and Jung, Ken and Kale, David and Ranganath, Rajesh and Wallace, Byron and Wiens, Jenna}, volume = {85}, series = {Proceedings of Machine Learning Research}, month = {17--18 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v85/engelhard18a/engelhard18a.pdf}, url = {https://proceedings.mlr.press/v85/engelhard18a.html}, abstract = {Health risks from cigarette smoking – the leading cause of preventable death in the United States – can be substantially reduced by quitting. Although most smokers are motivated to quit, the majority of quit attempts fail. A number of studies have explored the role of self-reported symptoms, physiologic measurements, and environmental context on smoking risk, but less work has focused on the temporal dynamics of smoking events, including daily patterns and related nicotine effects. In this work, we examine these dynamics and improve risk prediction by modeling smoking as a self-triggering process, in which previous smoking events modify current risk. Specifically, we fit smoking events self-reported by 42 smokers to a time-varying semi-parametric Hawkes process (TV-SPHP) developed for this purpose. Results show that the TV-SPHP achieves superior prediction performance compared to related and existing models, with incorporation of time-varying predictors having greatest benefit over longer prediction windows. Moreover, the impact function illustrates previously unknown temporal dynamics of smoking, with possible connections to nicotine metabolism to be explored in future work through a randomized study design. By more effectively predicting smoking events and exploring a self-triggering component of smoking risk, this work supports development of novel or improved cessation interventions that aim to reduce death from smoking.} }
Endnote
%0 Conference Paper %T Predicting Smoking Events with a Time-Varying Semi-Parametric Hawkes Process Model %A Matthew Engelhard %A Hongteng Xu %A Lawrence Carin %A Jason A. Oliver %A Matthew Hallyburton %A F. Joseph McClernon %B Proceedings of the 3rd Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2018 %E Finale Doshi-Velez %E Jim Fackler %E Ken Jung %E David Kale %E Rajesh Ranganath %E Byron Wallace %E Jenna Wiens %F pmlr-v85-engelhard18a %I PMLR %P 312--331 %U https://proceedings.mlr.press/v85/engelhard18a.html %V 85 %X Health risks from cigarette smoking – the leading cause of preventable death in the United States – can be substantially reduced by quitting. Although most smokers are motivated to quit, the majority of quit attempts fail. A number of studies have explored the role of self-reported symptoms, physiologic measurements, and environmental context on smoking risk, but less work has focused on the temporal dynamics of smoking events, including daily patterns and related nicotine effects. In this work, we examine these dynamics and improve risk prediction by modeling smoking as a self-triggering process, in which previous smoking events modify current risk. Specifically, we fit smoking events self-reported by 42 smokers to a time-varying semi-parametric Hawkes process (TV-SPHP) developed for this purpose. Results show that the TV-SPHP achieves superior prediction performance compared to related and existing models, with incorporation of time-varying predictors having greatest benefit over longer prediction windows. Moreover, the impact function illustrates previously unknown temporal dynamics of smoking, with possible connections to nicotine metabolism to be explored in future work through a randomized study design. By more effectively predicting smoking events and exploring a self-triggering component of smoking risk, this work supports development of novel or improved cessation interventions that aim to reduce death from smoking.
APA
Engelhard, M., Xu, H., Carin, L., Oliver, J.A., Hallyburton, M. & McClernon, F.J.. (2018). Predicting Smoking Events with a Time-Varying Semi-Parametric Hawkes Process Model. Proceedings of the 3rd Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 85:312-331 Available from https://proceedings.mlr.press/v85/engelhard18a.html.

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