Model predicts patient breathing support effects during critical care
Learning response-aware patient dynamics for respiratory support
Artificial Intelligence
Summary
Breathing support helps critically ill patients, but different patients respond differently over time. The authors created a model that better understands how patients’ functions change with breathing support, by separating normal changes from those caused by the support. This helps predict how a patient’s condition might evolve, especially when their health is actively changing. The model was tested on data from two hospitals and showed improved predictions in these situations.
What this means in practice
- •For intensive care unit clinicians: Assess how different respiratory support options may affect patient trajectories to inform personalized treatment decisions.
- •For clinical data engineers: Integrate the response-aware dynamics model into ICU monitoring systems to better track and predict patient physiological changes.
Authors
Xiaolei Lu, Shamim Nemati
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.