强化学习以预防医疗补助人口中的急性护理事件:混合方法研究
Sanjay Basu1, Bhairavi Muralidharan1, Parth Sheth1
1Waymark, San Francisco, CA, United States.
JMIR AI
|October 8, 2025
概括
使用SARSA的强化学习 (RL) 改善了复杂患者的护理管理,与标准方法相比,急性事件减少了12%. 这种人工智能方法也提高了人口群体之间的公平性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 多学科团队需要对具有复杂医疗和社会需求的患者进行高效的优先考虑.
- 目前的护理管理依赖于经验,错过了数据驱动干预建议的机会.
- 推系统可以通过从患者的发展轨迹中学习来预防不良结果.
研究的目的:
- 评估强化学习 (RL) 方法是否可以在推最佳干预措施方面超越标准护理管理.
- 将SARSA RL模型与复杂患者护理的基于经验的方法进行比较.
主要方法:
- 将SARSA RL模型与标准基于经验的方法进行比较,使用3175名医疗补助受益人的数据.
- 利用反事实性因果推断来估计急性护理事件的减少.
- 评估了人口统计学子组的模型公平性,并进行了定性图表审查.
主要成果:
- 与现状相比,SARSA指导的护理管理使急性护理事件减少了12个百分点 (20.7%的相对减少).
- 通过RL方法,在性别和种族/种族小组之间提高了公平性.
- 定性审查显示,SARSA确定了医疗社会相互作用的干预措施,例如住房质量和粮食不安全.
结论:
- 与标准实践相比,SARSA指导的护理管理显示出减少急性护理利用率的承诺.
- 强化学习可以增强患者的复杂决策与并发的临床和社会因素.
- RL方法在不同患者群体中保持安全性和公平性.
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