AI 中文总结
针对呼吸支持中患者生理轨迹差异问题，提出响应感知动力学模型，显式分解生理变化并锚定室内空气参考，在ICU队列中实现更一致的轨迹预测。
AI 中文摘要
呼吸支持可以塑造危重患者短期生理轨迹，但接受相同干预的患者可能遵循不同的生理轨迹。临床患者动力学模型通常根据近期生理和记录的干预措施预测未来状态，而生理变化主要通过预测的未来状态来表示。我们提出了一种响应感知的患者动力学模型，在自回归状态更新过程中显式地表示生理变化。该模型将预测的生理变化分解为状态依赖的基线动力学和与呼吸支持相关的偏差，并以室内空气作为分解的参考。我们对该参考锚定公式进行了形式化分析。一条响应路径编码预测的生理变化，并利用它来更新预测范围内的潜在患者状态。在两个独立机构的ICU队列中，所提出的模型实现了与患者动力学基线相当的整体轨迹预测，并在生理状态变化时表现出更一致的改进。
英文摘要
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.
CommentsUnder review
