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基于生活日志数据的虚拟连续葡萄糖监测和葡萄糖预测的深度学习框架
Min Hyuk Lim1,2, Hyocheol Chae3, Jeongwon Yoon3
1Graduate School of Health Science and Technology, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan, 44919, Republic of Korea. minhyuk.md@gmail.com.
Scientific reports
|May 10, 2025
概括
这项研究引入了一个深度学习模型,用于使用生命日志数据进行葡萄糖预测,即使没有先前的葡萄糖读数. 这种方法支持在中断期间持续监测葡萄糖 (CGM).
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
- 代谢健康研究 代谢健康研究
背景情况:
- 持续葡萄糖监测 (CGM) 对代谢健康至关重要,但由于成本和用户负担,它面临采用障碍,导致监测中断.
- 现有的CGM系统通常需要连续的葡萄糖数据来准确预测.
- 有效管理代谢健康需要可靠的葡萄糖水平跟踪,即使标准监测是不可行的.
研究的目的:
- 开发和验证一种新的深度学习框架,以使用全面的生命日志数据推断当前的葡萄糖水平,独立于先前的葡萄糖测量.
- 评估模型的预测准确性及其支持间歇性监测场景的潜力.
- 从模型中探索潜伏表示的实用性,以了解葡萄糖模式.
主要方法:
- 开发了一个深度学习框架,使用双向长短期记忆 (LSTM) 网络与编码器-解码器架构和双重注意力机制.
- 该模型在171名健康成年人的广泛生活日志数据 (饮食,体育活动) 和葡萄糖测量上进行了训练.
- 分析了编码器的潜伏表示,以确定与葡萄糖和生命日志序列相关的模式.
主要成果:
- 该模型实现了根平均平方误差 (RMSE) 的19.49±5.42 (mg/dL),相关系数为0.43±0.2,平均绝对百分比误差 (MAPE) 的12.34±3.11 (%).
- 这些结果是为了当前的葡萄糖水平预测而获得的,而不是依赖任何在推断阶段之前的葡萄糖信息.
- 潜伏表征的分析表明了区分葡萄糖模式的可能性.
结论:
- 拟议的深度学习模型只能使用生命日志数据准确预测葡萄糖水平,在CGM中断期间提供可行的替代方案.
- 这一框架有可能提高葡萄糖监测的可用性和可访问性,特别是在间歇性情景中.
- 这些发现表明,全面的生命日志数据对代谢健康管理具有显著的预测能力.
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