  Medical practitioners use survival models to explore and understand the
relationships between patients' covariates (e.g. clinical and genetic features)
and the effectiveness of various treatment options. Standard survival models
like the linear Cox proportional hazards model require extensive feature
engineering or prior medical knowledge to model treatment interaction at an
individual level. While nonlinear survival methods, such as neural networks and
survival forests, can inherently model these high-level interaction terms, they
have yet to be shown as effective treatment recommender systems. We introduce
DeepSurv, a Cox proportional hazards deep neural network and state-of-the-art
survival method for modeling interactions between a patient's covariates and
treatment effectiveness in order to provide personalized treatment
recommendations. We perform a number of experiments training DeepSurv on
simulated and real survival data. We demonstrate that DeepSurv performs as well
as or better than other state-of-the-art survival models and validate that
DeepSurv successfully models increasingly complex relationships between a
patient's covariates and their risk of failure. We then show how DeepSurv
models the relationship between a patient's features and effectiveness of
different treatment options to show how DeepSurv can be used to provide
individual treatment recommendations. Finally, we train DeepSurv on real
clinical studies to demonstrate how it's personalized treatment recommendations
would increase the survival time of a set of patients. The predictive and
modeling capabilities of DeepSurv will enable medical researchers to use deep
neural networks as a tool in their exploration, understanding, and prediction
of the effects of a patient's characteristics on their risk of failure.
