Deep Survival Analysis


Rajesh Ranganath, Adler Perotte, Noémie Elhadad, David Blei ;
Proceedings of the 1st Machine Learning for Healthcare Conference, PMLR 56:101-114, 2016.


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 introduce deep survival analysis, a hierarchical generative approach to survival analysis in the context of the EHR. It departs from previous approaches in two main 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 handles heterogeneous data types that occur in the EHR. We validate deep survival analysis by stratifying patients according to risk of developing coronary heart disease (CHD) on 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 superior in stratifying patients according to their risk.

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