发表机构
Broad Institute of MIT and Harvard; Harvard University; Brigham and Women’s Hospital; Technion–Israel Institute of Technology（MIT和哈佛大学Broad研究所; 哈佛大学; 布莱根妇女医院; 技术学院-以色列理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对脓毒症治疗策略有争议且现有强化学习方法适应性不足的问题，提出EHR-MPC框架，通过训练生成式电子健康记录模型形式的患者数字孪生解耦学习与治疗优化，经模拟评估性能优于强化学习基线，建立了决策通用框架。
AI 中文摘要
脓毒症是主要死因，但最佳治疗策略仍有争议。现有强化学习方法学习固定策略，限制了推理时对变化临床目标的适应性。我们提出EHR-MPC框架，通过训练生成式电子健康记录模型形式的患者数字孪生，将学习患者动态与优化治疗解耦。数字孪生预测干预下的临床轨迹，使模型预测控制通过推理时模拟规划优化治疗。我们用离策略重要性采样和基于策略的模拟评估在多中心ICU脓毒症队列上评估EHR-MPC。相对于强化学习基线，EHR-MPC实现了可比的离策略性能和更好的模拟性能。不同于强化学习，这项工作将脓毒症治疗优化框架化为对学习到的患者动态的推理时控制，建立了使用生成式临床模型进行决策的通用框架。
英文摘要
Sepsis is a leading cause of mortality, yet optimal treatment policies remain contested. Existing reinforcement learning (RL) approaches learn fixed strategies for sepsis treatment, limiting adaptability to changing clinical objectives during inference. We propose EHRMPC, a framework that decouples learning patient dynamics from optimizing treatment by training a patient digital twin in the form of a generative electronic health record (EHR) model. The digital twin predicts clinical trajectories under interventions and enables model predictive control (MPC) to optimize treatments via inference-time planning over simulations. We evaluate EHR-MPC on a multicenter ICU sepsis cohort spanning 8 hospitals in the Mass General Brigham health system using both off-policy importance sampling and on-policy simulation-based evaluation. Relative to RL baselines, EHR-MPC achieves comparable off-policy performance and improved simulation performance. Unlike RL, this work frames sepsis treatment optimization as inference-time control over learned patient dynamics, establishing a general framework for decision making with generative clinical models.
