 DeepServe is a cox-proportional hazards deep neural network that models interactions between a patient's covariates, clinical and genetic features, and treatment effectiveness to provide personalized treatment recommendations. It outperforms other state-of-the-art survival methods in simulated and real survival data, successfully modeling increasingly complex relationships between a patient's features and risk of failure. DeepServe can also model the relationship between a patient's features and effectiveness of different treatment options to provide individualized treatment recommendations. By using 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, medical researchers can increase the survival time of patients. This article was authored by Jared L. Katzman, Uri Shoham, Alexander Kloninger, and others. We are article.tv, links in the description below.