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 ad-versarial 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 in-formation 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.
|Original language||English (US)|
|Title of host publication||35th International Conference on Machine Learning, ICML 2018|
|Publisher||International Machine Learning Society (IMLS)email@example.com|
|Number of pages||14|
|State||Published - Jan 1 2018|