一种基于基于学习的模型预测控制的实用方法,对人工胰腺患者的预先知识最小
1Transdisciplinary Department of Medicine & Advanced Technology, Seoul National University Hospital, 101 Daehak-ro, Jongro-gu, Seoul 03080, Republic of Korea; Institute of Medical and Biological Engineering, Seoul National University, Seoul 03080, Republic of Korea.
Computer methods and programs in biomedicine
|July 6, 2023
概括
这项研究引入了一种新的人工胰腺系统,可以有效调节1型糖尿病患者的血糖水平,使用最少的先前数据. 该系统集成基于学习的模型预测控制 (MPC) 进行自适应性胰岛素输送,改善时间范围内的指标.
科学领域:
- 生物医学工程 生物医学工程
- 控制系统 控制系统
- 糖尿病 技术 技术
背景情况:
- 准确的葡萄糖动态识别用于糖尿病管理通常需要广泛的患者数据和临床程序.
- 这些先决条件并不总是可行的,这给个性化糖尿病护理带来了挑战.
研究的目的:
- 开发一种实用的人工胰腺方法,需要最低限度的患者特异性先前知识.
- 整合基于学习的模型预测控制 (MPC) 与自适应的基底/球体胰岛素注射和悬浮,以改善葡萄糖调节.
主要方法:
- 定期更新的葡萄糖动态系统矩阵仅使用输入值,没有预先训练的模型.
- 通过基于学习的MPC算法计算最佳胰岛素剂量,包括餐点检测和估计.
- 根据前一天的血糖控制表现,精心调整基底和玻尿酸胰岛素注射.
- 在1型糖尿病代谢模拟器中使用20名虚拟患者验证了该方法.
主要成果:
- 实现了90.8%的时间在范围 (TIR) 完全宣布的餐.
- 保持了85.2%的TIR,即使错过了三分之一的饭菜公告.
- 时间低于范围 (TBR) 仍然很低,为0.3%的全餐公告和0.9%的错过公告.
结论:
- 拟议的方法有效调节血糖,而不需要对患者进行广泛的先前测试.
- 证明了临床知识和基于学习的模块在实际人工胰腺实施的控制框架内成功集成,使用最小的患者数据.
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