手术优先级的电子健康战略:基于数字双胞胎和强化学习的方法
Fabián Silva-Aravena1, Jenny Morales1, Manoj Jayabalan2
1Facultad de Ciencias Sociales y Económicas, Universidad Católica del Maule, Avenida San Miguel 3605, Talca 3460000, Chile.
Bioengineering (Basel, Switzerland)
|June 26, 2025
概括
本研究引入了使用数字双胞胎 (DTs) 和强化学习 (RL) 来实现更智能的外科手术安排的新框架. 该方法显著减少了等待时间和临床风险,同时提高了手术室的效率,并优先考虑弱势患者.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 运营研究 运营研究
背景情况:
- 传统的选择性手术安排在效率和公平性方面面临挑战.
- 需要动态优先级来适应实时患者和系统变量.
- 现有的系统往往缺乏透明度和适应性.
研究的目的:
- 为选择性外科手术安排提出一种新的方法框架.
- 将患者特定的数字双胞胎 (DTs) 与强化学习 (RL) 进行集成,以实现动态优先级.
- 建立智能电子健康平台的基础,支持公平的医疗保健服务.
主要方法:
- 开发了一个结合数字双胞胎 (DTs) 和强化学习 (RL) 的框架.
- 模拟临床,经济,行为和社会变量,用于实时优先级评分.
- 使用强化学习引擎来优化患者访问,并最大限度地提高长期系统性能.
- 模拟的手术安排场景与合成患者数据进行验证.
主要成果:
- 平均患者等待时间减少了55.1%.
- 在手术时减少了41.9%的临床风险.
- 手术室 (OR) 的利用率增加了16.1%.
- 在优先考虑社会脆弱患者方面显著增加.
结论:
- 拟议的框架比传统的外科手术调度策略提供了实质性的改进.
- 数据技术和RL的整合提供了一个透明,适应性和伦理一致的决策支持架构.
- 这种方法作为未来手术安排中的智能医疗保健平台的宝贵基础.
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