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通过持续的葡萄糖和体力活动监测以及深度学习,准确,个性化的葡萄糖预测平台
Deepjyoti Kalita1, Hrishita Sharma1, Jayanta Kumar Panda2
1National Institute of Technology, Rourkela, Sundargarh, 769008, Odisha, India.
Medical engineering & physics
|October 20, 2024
概括
这项研究引入了一种混合深度学习模型,用于在糖尿病管理中准确预测葡萄糖. 该系统包括一个移动应用程序,通过结合体育活动数据来改善预测,有助于避免高或低血糖水平.
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
- 内分泌学 在内分泌学.
背景情况:
- 持续的葡萄糖监测 (CGM) 对人工胰腺 (AP) 系统和一般糖尿病管理至关重要.
- 深度学习模型可以预测葡萄糖水平,通过优化AP中的胰岛素剂量来帮助预防高血糖和低血糖.
研究的目的:
- 提出一种新的混合深度学习框架,具有预测葡萄糖测量的注意力机制.
- 开发基于云计算的系统和移动应用程序,用于收集各种患者数据 (CGM,体力活动,碳水化合物,胰岛素),以推动葡萄糖预测.
- 为了验证该模型对1型和2型糖尿病患者的有效性.
主要方法:
- 开发了一个混合深度学习框架,整合了基于内容的注意力学习.
- 创建了一个基于云的系统和移动应用程序,用于实时数据收集.
- 该模型在有限的 (8天) 个体患者数据上进行了训练,并在随后的几天中进行了验证.
主要成果:
- 该模型在不同的预测时间范围内 (15,30,60分钟) 实现了准确的葡萄糖预测.
- 俄俄T1DM数据集的性能指标包括在30分钟时7.14±1.76%的MARD和在60分钟时12.33±3.15%的MARD.
- 纳入体力活动数据显著提高了葡萄糖预测的准确性.
- 该模型表现出强的性能,即使在单个患者的有限数据上进行训练时也是如此.
结论:
- 拟议的混合深度学习模型有效预测1型和2型糖尿病患者的葡萄糖水平.
- 集成的云和移动系统通过准确的葡萄糖预测来促进个性化糖尿病管理.
- 使用有限的患者数据进行个性化葡萄糖预测,有望改善人工胰腺功能和整体糖尿病护理.
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