使用非线性自回归与外源输入神经网络 (NNARX) 在血糖预测
1Automatic control department, Tabin Institute For Metallurgical Studies, Iron and Steel street, Tabbin, Helwan, Cairo, Egypt. dr-fayrouz@tims.gov.eg.
Bioelectronic medicine
|April 16, 2024
概括
一个改进的神经网络 (NNARX) 提高了对更长时间的血糖预测准确度,有助于糖尿病管理. 这种增强的预测有助于通过提供更可靠的葡萄糖度预测来预防低血糖和高血糖.
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:
- 准确的血糖 (BG) 预测对于有效的糖尿病管理系统至关重要.
- 持续葡萄糖监测 (CGM) 的进步为血糖控制提供了新的可能性.
- 较长的预测视界对于预防低血糖和高血糖是必不可少的.
研究的目的:
- 提出一个非线性自回归外源输入神经网络 (NNARX) 以提高BG预测.
- 与传统的循环神经网络 (RNN) 相比,提高更长的预测视界 (PHs) 的预测准确性和稳定性.
主要方法:
- 一个修改后的NNARX模型是基于以前建立的RNN架构开发的.
- 在NNARX模型中,使用了不同的初始化和构建技术.
- 模型开发包括在关闭控制循环之前在MATLAB中构建和训练非线性自回归外源输入模型.
主要成果:
- 拟议的NNARX与45分钟以上PH的未经修改的RNN相比,显示出更高的预测准确性和稳定性.
- 在较长的PH中观察到预测准确度的显著改善.
结论:
- 使用NNARX进行RNN修改,可将预测能力扩展到100分钟.
- 在100分钟的预测中,FIT和RMSE值的统计学上显著改善.
- 对于45分钟和60分钟的预测间隔,NNARX模型减少了根平均平方误差 (RMSE).
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