一种混合闭环血糖控制算法,具有基于深度强化学习和模型预测控制的安全限制值
Shanyong Huang1, Yusheng Fu1, Shaowei Kong1
1School of Information and Communication Engineering, University of Electronic Science and Technology, Chengdu 611731, China.
Biosensors
|January 27, 2026
概括
本研究引入了一种更安全的深度强化学习 (DRL) 方法用于糖尿病治疗,通过将DRL与模型预测控制 (MPC) 结合起来. 这种混合方法改善了糖尿病患者的血糖控制,提高了安全性和有效性.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 内分泌学 在内分泌学.
背景情况:
- 糖尿病患者的血糖管理是复杂的,因为生理变化和挑战与传统的胰岛素控制算法.
- 深度强化学习 (DRL) 对糖尿病治疗具有前景,但由于其试错性质,它面临安全问题.
- 对于糖尿病管理的DLR的临床应用受到高安全标准的需要的阻碍.
研究的目的:
- 为糖尿病患者开发一种更安全,更有效的胰岛素剂量控制算法.
- 通过将其与模型预测控制 (MPC) 整合起来,解决DRL在临床糖尿病治疗中的安全挑战.
- 为了改善血糖调节,在70-180 mg/dL的目标范围内.
主要方法:
- 一种混合控制策略,将深度强化学习 (DRL) 与模型预测控制 (MPC) 结合起来.
- 利用患者互动数据来训练MPC的血糖预测模型.
- 整合一个安全控制器来限制DRL的行动,并防止不安全的血糖水平.
- 使用FDA批准的UVA/Padova葡萄糖动力学模拟器进行验证.
主要成果:
- 拟议的DRL-MPC混合模型在成年患者的健康血糖范围内达到72.51%的时间.
- 与基线模型相比,这代表了2.54%的改善.
- 没有观察到严重高血糖或低血糖事件的增加,这表明安全性提高.
结论:
- 联合DRL和MPC方法为自动化胰岛素剂量控制提供了更安全,更有效的方法.
- 这种混合策略成功地解决了在临床环境中纯DRL的安全限制.
- 这些发现代表了人工智能在糖尿病管理中安全可靠的临床应用的重要一步.
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