# Deep Physiological State Space Model for Clinical Forecasting

arXiv: https://arxiv.org/abs/1912.01762 · HTML: https://mlsanity.com/paper/1912.01762/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2019.

## Built on by (most cited first, 2 of 2)

- [Deep State-Space Generative Model For Correlated Time-to-Event Predictions](https://mlsanity.com/paper/2407.19371/) (2024)
- [Learning to Select the Best Forecasting Tasks for Clinical Outcome Prediction](https://mlsanity.com/paper/2407.19359/) (2024)

## Abstract (arXiv)

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.