ATTE:一种创新的集体学习方法,具有年龄组指导的微调,用于改进个性化的血压估计
IEEE transactions on neural networks and learning systems
|February 11, 2026
概括
精准医学需要准确的无袖血压 (BP) 估计. 一种使用年龄匹配模型的新ATTE方法,在使用最小数据的情况下显著提高了血压精度,提高了医疗监测.
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
- 心血管监测 心血管监测
背景情况:
- 精准医学需要对医疗保健进行准确的无袖血压 (BP) 估计.
- 转移学习已经推进了个性化的血压估计,但往往忽视了年龄特定的生理差异.
- 传统的微调方法限制了个性化BP估计模型的有效性.
研究的目的:
- 引入一种新的两级集体学习方法 (ATTE),用于无袖手 BP 估计.
- 为了验证与年龄相匹配的预训练改善了精细调整BP估计的原则.
- 为了提高个性化BP估计的准确性和减少不确定性.
主要方法:
- 开发了一种两级集体学习方法 (ATTE),集成多个基于脉冲波的BP模型.
- 实施特征级合体学习 (FEL) 来结合特定年龄组的模型.
- 利用优化贝叶斯模型平均值 (BMA) 进行模型输出的自适应加权.
主要成果:
- 在使用仅50个转移样本的公共数据集上,ATTE提高了系统性和透气性血压估计精度6.55%和5.58%.
- 在自我收集的数据集上实现了5.10%和8.30%的准确性改进,在老年护理中进行了验证.
- 废弃性研究证实了年龄匹配微调原理以及ATTE的有效性和通用性.
结论:
- 与年龄相匹配的微调原理显著提高了个性化的BP估计.
- 该ATTE方法提供了一个强大的和准确的方法来无袖式BP估计.
- 这项工作在个性化监测中为现实世界医疗保健应用提供了巨大的潜力.
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