用强化学习分析重症患者治疗差异，发现指南实施后仍存在照护不均。
Identifying Differential Patient Care Through Inverse Intent Inference
- 通过逆向强化学习从专家诊疗数据中提取最优治疗策略。
- 在未见人群上估算反事实疗效，发现不同亚组治愈率差异显著。
- 适合医疗质量研究者与政策制定者参考，助力消除诊疗差距。
脓毒症是一种由宿主对感染的失调反应导致终末器官功能障碍的危重疾病。尽管生存脓毒症运动已发布治疗指南以统一和标准化脓毒症患者的照护，但多项研究指出，在急诊科和重症监护室的患者诊疗过程中仍存在显著的照护差异。本文应用行为克隆、模仿学习及逆向强化学习等强化学习技术，基于专家示范数据学习特定脓毒症亚组的最优治疗策略。随后，将模型应用于另一组未见的医疗人群，估算反事实最优治疗策略，并通过与实际策略对比，识别治疗差异。数据来自MIMIC-IV中的脓毒症队列及麻省总医院布里格姆医疗系统的临床数据仓库。本研究旨在利用学习到的最优策略函数，评估反事实治疗方案，识别目标亚组间的照护差异。我们希望该方法能揭示治疗照护中的不公平现象，并追踪国家脓毒症治疗指南发布后的疗效变化。
原文摘要 · Abstract (English)
Sepsis is a life-threatening condition defined by end-organ dysfunction due to a dysregulated host response to infection. Although the Surviving Sepsis Campaign has launched and has been releasing sepsis treatment guidelines to unify and normalize the care for sepsis patients, it has been reported in numerous studies that disparities in care exist across the trajectory of patient stay in the emergency department and intensive care unit. Here, we apply a number of reinforcement learning techniques including behavioral cloning, imitation learning, and inverse reinforcement learning, to learn the optimal policy in the management of septic patient subgroups using expert demonstrations. Then we estimate the counterfactual optimal policies by applying the model to another subset of unseen medical populations and identify the difference in cure by comparing it to the real policy. Our data comes from the sepsis cohort of MIMIC-IV and the clinical data warehouses of the Mass General Brigham healthcare system. The ultimate objective of this work is to use the optimal learned policy function to estimate the counterfactual treatment policy and identify deviations across sub-populations of interest. We hope this approach would help us identify any disparities in care and also changes in cure in response to the publication of national sepsis treatment guidelines.
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