基于代学习控制架构的血糖预测的二维模型框架
Shuang Wen1, Hongru Li2, Rui Tao1
1College of Information Sciences and Engineering, Northeastern University, No. 11 St. 3, Wenhua Road, Heping District, Shenyang, 110819, People's Republic of China.
Medical & biological engineering & computing
|July 3, 2023
概括
预测未来的血糖 (BG) 水平对于糖尿病管理至关重要. 使用神经网络的新2D模型框架提高了BG预测的准确性,并通过考虑每日和每日间的血糖动态来减少延迟.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 糖尿病 技术 技术
背景情况:
- 准确,个性化的血糖 (BG) 预测对于促进糖尿病管理至关重要.
- 人类的昼夜节律和生活方式影响日常血糖动态,为预测建模提供了潜力.
研究的目的:
- 开发一个先进的模型框架,准确及时预测未来的血糖水平.
- 为了提高预测准确性,利用白天和白天的血糖数据.
主要方法:
- 开发了一个以代学习控制 (ILC) 为灵感的二维 (2D) 模型框架.
- 辐射基函数神经网络被用来捕捉葡萄糖代谢中的非线性.
- 在各种预测视野 (PHs) 的in silico数据集上构建并验证了特定患者的模型.
主要成果:
- 2D框架显示,对血糖水平的预测准确性增加.
- 该模型成功地减少了预测未来血糖趋势的延迟.
- 该方法有效地整合了短距离 (一天内) 和长距离 (一天内) 血糖信息.
结论:
- 拟议的二维建模框架为血糖预测提供了一种新的方法.
- 这种方法增强了个性化的葡萄糖管理,包括在低血糖预警和血糖控制中的应用.
- 这些发现有助于开发更复杂的糖尿病管理技术.
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