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

TL;DR

This paper introduces a deep state-space generative model that captures interdependent clinical events over time, improving the accuracy of survival predictions and revealing meaningful correlations among health outcomes.

Contribution

It presents a novel deep latent state-space model with a discrete-time hazard function for correlated time-to-event predictions, outperforming existing methods.

Findings

Improved accuracy in survival distribution estimation.

Uncovered meaningful correlations among clinical events.

Outperformed state-of-the-art baselines in real EMR data.

Abstract

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.