在高复杂性医院优化MRI调度:数字双胞胎和强化学习方法
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
本研究介绍了一个数字双胞胎和强化学习框架,以优化磁共振成像 (MRI) 调度. 智能系统显著提高了机器的使用,减少了患者等待时间,提高了医疗保健的效率.
科学领域:
- 医疗保健 运营 研究 研究 研究
- 人工智能在医学中的应用
- 医学成像信息学 医疗成像信息学
背景情况:
- 高复杂度的医院面临着MRI运营效率低下的问题:机器利用率低,患者等待时间长,以及不公平的优先访问.
- 智能调度对于基于临床紧迫性和资源优化等待列表的动态管理至关重要.
研究的目的:
- 开发和评估一个新的框架,将数字双胞胎 (DT) 与强化学习 (RL) 代理集成在一起,以优化MRI操作调度.
- 提高MRI机器的利用率,减少患者等待时间,并根据临床优先级确保公平的服务提供.
主要方法:
- 一个数字双胞胎模拟的MRI操作动态使用现实世界的参数 (患者的到来,检查时间,机器的可靠性,优先级).
- 通过深度Q网络 (DQN) 进行训练的强化学习代理学习了调度策略,以优化关键绩效指标.
- 该框架与传统的调度启发方式 (如"先来先服务" (FCFS)) 相比进行了评估.
主要成果:
- 在MRI机器利用率上实现了14.5%的增长.
- 减少了44.8%的患者平均等待时间.
- 与基线方法相比,在优先权加权公平性方面取得了显著的改进.
结论:
- 拟议的DT-RL框架为优化复杂医院环境中的MRI服务提供了一个可扩展和可适应的解决方案.
- 这种方法通过提高运营效率和公平性来提高患者满意度和临床结果.
- 该研究强调了先进的人工智能和模拟技术在改变医疗保健运营中的潜力.
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