Deep Physiological State Space Model for Clinical Forecasting

TL;DR

This paper introduces a deep state space model that captures patient condition dynamics and interventions from EMR data, enabling accurate forecasting of future clinical measurements and treatments.

Contribution

It presents a novel intervention-augmented deep state space generative model that explicitly models latent patient states and their interactions with clinical interventions.

Findings

Outperforms several state-of-the-art methods on real EMR data

Accurately predicts future clinical measurements and interventions

Effectively captures complex temporal correlations in patient data

Abstract

Clinical forecasting based on electronic medical records (EMR) can uncover the temporal correlations between patients' conditions and outcomes from sequences of longitudinal clinical measurements. In this work, we propose an intervention-augmented deep state space generative model to capture the interactions among clinical measurements and interventions by explicitly modeling the dynamics of patients' latent states. Based on this model, we are able to make a joint prediction of the trajectories of future observations and interventions. Empirical evaluations show that our proposed model compares favorably to several state-of-the-art methods on real EMR data.