深度强化学习用于多个目标的propofol剂量
Zheyan Tu1, Sean Jeffries1, Eric Pelletier1
1Department of Surgical and Interventional Sciences, McGill University Health Center, Montreal, Quebec, Canada.
Journal of clinical monitoring and computing
|March 5, 2025
概括
深度强化学习自动化了用于麻醉的propofol剂量. 双延迟深度决定性政策梯度 (TD3) 算法有效控制了多个生理参数,提高了患者的安全性.
科学领域:
- 麻醉学和医疗信息学
- 人工智能在医学中的应用
背景情况:
- 用于麻醉的propofol需要对患者生理学的复杂管理.
- 保持关键的生理参数在安全范围内是一项挑战.
研究的目的:
- 通过使用深度强化学习 (DRL) 来自动化普罗波福尔剂量.
- 在目标和安全范围内保持多个生理参数 (BIS,HR,RR,MAP).
主要方法:
- 开发了一种多变量药理动力学-药理动力学 (PK/PD) 模拟环境,用于对普罗波的影响.
- 设计了一个可调节的奖励系统,用于多目标麻醉输注.
- 使用双延迟深度决定性政策梯度 (TD3) 算法训练了一个DRL代理.
主要成果:
- TD3 DRL剂在精确和安全地控制生理参数方面表现出卓越的性能.
- TD3的性能优于其他DRL算法和传统的控制方法.
- 该系统成功地保持了BIS,HR,RR和MAP在所需范围内.
结论:
- DRL,特别是TD3算法,为自动化普罗波剂量提供了一个有前途的方法.
- 这种方法增强了麻醉期间生理参数的管理.
- 自动施用普罗波可以提高镇静和全身麻醉的安全性和有效性.
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