优化循环利尿治疗以减少急性喘息患者的死亡率,使用实用离线强化学习管道为医疗保健:回顾性单中心模拟研究
Jung Min Lee1, Shengpu Tang1,2, Michael Sjoding3
1Division of Computer Science and Engineering, College of Engineering, University of Michigan, 2260 Hayward St, Ann Arbor, MI, 48109, United States, 1 7346474832.
JMIR medical informatics
|October 10, 2025
概括
线下强化学习 (RL) 的新管道改善了循环利尿剂的临床决策. 这种方法通过优化治疗政策与标准临床医生行为相比,显著降低了医院内死亡率.
科学领域:
- 临床信息学是一种临床信息学.
- 机器学习在医疗保健中的应用
- 强化学习应用程序的强化学习应用程序
背景情况:
- 离线强化学习 (RL) 越来越多地用于临床决策.
- 现有的方法缺乏标准化,导致过度配备的政策和不准确的评估.
- 需要一个强大的管道来提高可靠性和临床工作流集成.
研究的目的:
- 引入一个实用的管道,管道学习强化学习 (PROP-RL) 强化学习的强有力的政策,用于临床环境中的线下RL.
- 提高政策的稳定性,并尽量减少对临床工作流程的干扰.
- 证明在优化循环利尿剂治疗医院患者的有效性.
主要方法:
- 模拟循环利尿剂管理作为一个线下RL问题,使用电子健康记录数据.
- 定义了一个离散状态空间,二元动作空间 (利尿剂使用) 和医院死亡奖励函数.
- 训练并评估了大量成年住院患者的政策,与临床医生的行为进行比较.
主要成果:
- 这项研究包括36570例住院治疗.
- 学习政策与大多数州的临床医生一致,但在两个州有所分歧.
- 在分歧的情况下,RL政策显著降低了死亡率,从3.8%降至2.2% (1.6%的降低,P=.006).
结论:
- PROP-RL管道为顺序处理选择中的离线RL提供了一个强大的方法.
- 强调国家代表和政策评估在临床RL中的重要性.
- 确定当前循环利尿剂治疗策略的潜在改进,并为未来临床RL应用提供蓝图.
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