面向呼吸支持的响应感知患者动态模型研究
Learning response-aware patient dynamics for respiratory support
AI 导读
研究者提出一种响应感知的患者动态模型，在自回归状态更新中显式表示生理变化，并将预测的生理变化分解为状态依赖的基线动态与呼吸支持相关偏差，以室内空气作为分解参考。该模型在来自两家独立机构的 ICU 队列上，总体轨迹预测与患者动态基线相当，但在生理状态发生变化时改进更一致。
正文 · 原文
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.
| Comments: | Under review |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.32782 [cs.AI] |
| (or arXiv:2609.32782v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2609.32782 arXiv-issued DOI via DataCite |
Submission history
From: Lu Xiaolei [view email]
[v1]
Sat, 26 Sep 2026 16:56:52 UTC (673 KB)
[v2]
Tue, 27 Oct 2026 16:42:43 UTC (673 KB)
来源：arXiv cs.AI · arxiv.org