Deep State-Space Generative Model For Correlated Time-to-Event Predictions

Capturing the inter-dependencies among multiple types of clinicallycritical Time-to-event prediction (also known as survival analysis) investigates events is critical not only to accurate future event prediction, the distribution of time duration until the event of interest but also to better treatment planning. In this work, we propose a happens in the presence of event censorship. In the healthcare domain, deep latent state-space generative model to capture the interactions it is an essential tool for modeling the risks of critical medical among different types of correlated clinical events (e.g., kidney events and capturing of the relationship between the co-variants failure, mortality) by explicitly modeling the temporal dynamics and the risks [9]. of patients' latent states. Based on these learned patient states, we Recently, machine learning methods have been applied to timeto-event further develop a new general discrete-time formulation of the hazard predictions to provide flexible modeling of the time distribution rate function to estimate the survival distribution of patients [6, 19, 22], and capture the nonlinear relationship between with significantly improved accuracy.