用生成式病患数字孪生实现脓毒症治疗的实时调控
EHR-MPC: Inference-Time Control for Sepsis Treatment with Generative Patient Digital Twins
- 构建生成式电子病历模型作为病患数字孪生，模拟干预下的临床轨迹
- 在8家医院数据上，推理时规划表现优于强化学习基线
- 适合临床决策支持系统开发，可灵活适配不同治疗目标
脓毒症是导致死亡的主要原因，但最佳治疗策略仍存在争议。现有强化学习方法学习固定的治疗策略，难以在推理阶段适应变化的临床目标。我们提出EHR-MPC框架，将学习患者动态与优化治疗解耦，通过训练生成式电子健康记录（EHR）模型构建患者数字孪生。该数字孪生可预测干预下的临床轨迹，并利用模型预测控制（MPC）在推理时进行仿真规划以优化治疗。我们在包含8家医院的马萨诸塞总医院贝斯医疗体系多中心重症监护室脓毒症队列上评估该方法，采用离策略重要性采样和在策略仿真评估。相比强化学习基线，EHR-MPC在离策略评估中表现相当，在仿真评估中性能更优。不同于强化学习，本工作将脓毒症治疗优化建模为基于已学习患者动态的推理时控制，建立了一种通用的生成式临床模型决策框架。
原文摘要 · Abstract (English)
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.
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