多个数据集转移多任务学习,同时监测血糖和血压,使用共同的PPG功能
Noor Faris Ali1, Ibrahim M Elfadel2, Mohamed Atef1
1Electrical and Communication Engineering Department, College of Engineering, United Arab Emirates University, Al Ain, Abu Dhabi, 15551, United Arab Emirates.
Computers in biology and medicine
|June 24, 2025
概括
这项研究引入了多数据集学习方法,可以同时监测血糖和血压. 综合转移和多任务学习方法显著提高了两个生命体征的预测准确性.
科学领域:
- 生物医学工程 生物医学工程
- 生理监测 生理监测
- 医疗保健中的机器学习
背景情况:
- 同时监测血糖水平 (BGL) 和血压 (BP) 是至关重要的,但研究不足.
- BGL和BP之间的生理相互作用可以增强预测性学习.
- 具有双重BGL和BP标签的有限数据集阻碍了研究.
研究的目的:
- 建议和评估用于同时监测BGL和BP的新型多数据集 (MD) 学习方法.
- 通过利用来自多个来源的数据来应对有限的标记数据集的挑战.
- 调查转移学习 (TL) 和多任务学习 (MTL) 对这种双重监控任务的有效性.
主要方法:
- 从三个数据集 (一个源,两个目标) 中提取光聚体显微镜 (PPG) 的特征.
- 开发了三种MD学习策略:MD-TL,MD-MTL和MD-TL-MTL.
- 采用了沙普利增量解释 (SHAP) 和对模型可解释性的变量重要性.
主要成果:
- 拟议的MD-TL-MTL实现了平均绝对误差 (MAE) 的2.53±3.73对于缩血压,1.47±1.84对于腹缩血压,和1.36对于BGL.
- 克拉克错误网格分析显示了高精度,在A区的样本中99.86%的样本是A区.
- 在所有监测任务中,MD-TL-MTL的表现优于基线模型.
结论:
- 多数据集学习,特别是MD-TL-MTL方法,对于同时监测BGL和BP是有效的.
- 该研究成功地克服了使用转移和多任务学习的数据稀缺性挑战.
- 可解释性分析为预测模型提供了临床见解.
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