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Oral
Adversarial Time-to-Event Modeling
Paidamoyo Chapfuwa · Chenyang Tao · Chunyuan Li · Courtney Page · Benjamin Goldstein · Lawrence Carin · Ricardo Henao

Thu Jul 12 08:40 AM -- 08:50 AM (PDT) @ A7
in Deep Learning (Adversarial) 5 »

Modern health data science applications leverage abundant molecular and electronic health data, providing opportunities for machine learning to build statistical models to support clinical practice. Time-to-event analysis, also called survival analysis, stands as one of the most representative examples of such statistical models. We present a deep-network-based approach that leverages adversarial learning to address a key challenge in modern time-to-event modeling: nonparametric estimation of event-time distributions. We also introduce a principled cost function to exploit information from censored events (events that occur subsequent to the observation window). Unlike most time-to-event models, we focus on the estimation of time-to-event distributions, rather than time ordering. We validate our model on both benchmark and real datasets, demonstrating that the proposed formulation yields significant performance gains relative to a parametric alternative, which we also propose.

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Author Information

Paidamoyo Chapfuwa (Duke University)
Chenyang Tao (Duke University)
Chunyuan Li (Duke University)
Courtney Page (Duke University)
Benjamin Goldstein
Lawrence Carin (Duke)
Ricardo Henao (Duke University)

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