  Capturing the inter-dependencies among multiple types of clinically-critical
events is critical not only to accurate future event prediction, but also to
better treatment planning. In this work, we propose a deep latent state-space
generative model to capture the interactions among different types of
correlated clinical events (e.g., kidney failure, mortality) by explicitly
modeling the temporal dynamics of patients' latent states. Based on these
learned patient states, we further develop a new general discrete-time
formulation of the hazard rate function to estimate the survival distribution
of patients with significantly improved accuracy. Extensive evaluations over
real EMR data show that our proposed model compares favorably to various
state-of-the-art baselines. Furthermore, our method also uncovers meaningful
insights about the latent correlations among mortality and different types of
organ failures.
