Neural Conditional Event Time Models

Matthew Engelhard, Samuel Berchuck, Joshua D’Arcy, Ricardo Henao
Proceedings of the 5th Machine Learning for Healthcare Conference, PMLR 126:223-244, 2020.

Abstract

Event time models predict occurrence times of an event of interest based on known features. Recent work has demonstrated that neural networks achieve state-of-the-art event time predictions in biomedical applications, where event time models are frequently used, as well as a variety of other settings. However, standard event time models suppose that the event occurs, eventually, in all cases. Consequently, no distinction is made between a) the probability of event occurrence, and b) the predicted time of occurrence. This distinction is critical when predicting medical diagnoses as well as social media posts, equipment defects, and other events that or may not occur; and for which the features affecting a) may be different from those affecting b). In this work, we develop a conditional event time model that distinguishes between these components, implement it as a neural network with a binary stochastic layer representing nite event occurrence, and show how it may be learned from right-censored event times via maximum likelihood estimation. Results demonstrate improved event occurrence and event time predictions on synthetic data, medical events (MIMIC-III), and social media posts (Reddit), including posts related to mental health, comprising 21 total prediction tasks.

Cite this Paper

BibTeX
@InProceedings{pmlr-v126-engelhard20a, title = {Neural Conditional Event Time Models}, author = {Engelhard, Matthew and Berchuck, Samuel and D'Arcy, Joshua and Henao, Ricardo}, booktitle = {Proceedings of the 5th Machine Learning for Healthcare Conference}, pages = {223--244}, year = {2020}, editor = {Doshi-Velez, Finale and Fackler, Jim and Jung, Ken and Kale, David and Ranganath, Rajesh and Wallace, Byron and Wiens, Jenna}, volume = {126}, series = {Proceedings of Machine Learning Research}, month = {07--08 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v126/engelhard20a/engelhard20a.pdf}, url = {https://proceedings.mlr.press/v126/engelhard20a.html}, abstract = {Event time models predict occurrence times of an event of interest based on known features. Recent work has demonstrated that neural networks achieve state-of-the-art event time predictions in biomedical applications, where event time models are frequently used, as well as a variety of other settings. However, standard event time models suppose that the event occurs, eventually, in all cases. Consequently, no distinction is made between a) the probability of event occurrence, and b) the predicted time of occurrence. This distinction is critical when predicting medical diagnoses as well as social media posts, equipment defects, and other events that or may not occur; and for which the features affecting a) may be different from those affecting b). In this work, we develop a conditional event time model that distinguishes between these components, implement it as a neural network with a binary stochastic layer representing nite event occurrence, and show how it may be learned from right-censored event times via maximum likelihood estimation. Results demonstrate improved event occurrence and event time predictions on synthetic data, medical events (MIMIC-III), and social media posts (Reddit), including posts related to mental health, comprising 21 total prediction tasks.} }
Endnote
%0 Conference Paper %T Neural Conditional Event Time Models %A Matthew Engelhard %A Samuel Berchuck %A Joshua D’Arcy %A Ricardo Henao %B Proceedings of the 5th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2020 %E Finale Doshi-Velez %E Jim Fackler %E Ken Jung %E David Kale %E Rajesh Ranganath %E Byron Wallace %E Jenna Wiens %F pmlr-v126-engelhard20a %I PMLR %P 223--244 %U https://proceedings.mlr.press/v126/engelhard20a.html %V 126 %X Event time models predict occurrence times of an event of interest based on known features. Recent work has demonstrated that neural networks achieve state-of-the-art event time predictions in biomedical applications, where event time models are frequently used, as well as a variety of other settings. However, standard event time models suppose that the event occurs, eventually, in all cases. Consequently, no distinction is made between a) the probability of event occurrence, and b) the predicted time of occurrence. This distinction is critical when predicting medical diagnoses as well as social media posts, equipment defects, and other events that or may not occur; and for which the features affecting a) may be different from those affecting b). In this work, we develop a conditional event time model that distinguishes between these components, implement it as a neural network with a binary stochastic layer representing nite event occurrence, and show how it may be learned from right-censored event times via maximum likelihood estimation. Results demonstrate improved event occurrence and event time predictions on synthetic data, medical events (MIMIC-III), and social media posts (Reddit), including posts related to mental health, comprising 21 total prediction tasks.
APA
Engelhard, M., Berchuck, S., D’Arcy, J. & Henao, R.. (2020). Neural Conditional Event Time Models. Proceedings of the 5th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 126:223-244 Available from https://proceedings.mlr.press/v126/engelhard20a.html.

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