  The electronic health record (EHR) provides an unprecedented opportunity to
build actionable tools to support physicians at the point of care. In this
paper, we investigate survival analysis in the context of EHR data. We
introduce deep survival analysis, a hierarchical generative approach to
survival analysis. It departs from previous approaches in two primary ways: (1)
all observations, including covariates, are modeled jointly conditioned on a
rich latent structure; and (2) the observations are aligned by their failure
time, rather than by an arbitrary time zero as in traditional survival
analysis. Further, it (3) scalably handles heterogeneous (continuous and
discrete) data types that occur in the EHR. We validate deep survival analysis
model by stratifying patients according to risk of developing coronary heart
disease (CHD). Specifically, we study a dataset of 313,000 patients
corresponding to 5.5 million months of observations. When compared to the
clinically validated Framingham CHD risk score, deep survival analysis is
significantly superior in stratifying patients according to their risk.
