Capturing the inter-dependencies among multiple types of clinicallycritical 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.
