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We describe a new approach to estimating relative risks in time-to-eventprediction problems with censored data in a fully parametric manner. Ourapproach does not require making strong assumptions of constant proportionalhazard of the underlying survival distribution, as required by theCox-proportional hazard model. By jointly learning deep nonlinearrepresentations of the input covariates, we demonstrate the benefits of ourapproach when used to estimate survival risks through extensive experimentationon multiple real world datasets with different levels of censoring. We furtherdemonstrate advantages of our model in the competing risks scenario. To thebest of our knowledge, this is the first work involving fully parametricestimation of survival times with competing risks in the presence of censoring.

Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks