从住院患者数据开发的感染预测模型可以从可穿戴数据中预测出院COVID-19感染,当控制数据集转移时可穿戴数据
Ting Feng1, Sara Mariani2, Bryan Conroy2
1Philips North America, Cambridge, MA, USA. ting.feng@philips.com.
Scientific reports
|September 29, 2025
概括
适用于可穿戴设备的预训练临床决策支持算法可以早期检测COVID-19感染. 该系统在诊断测试前两天就能识别疾病,有助于早期发现疫情.
科学领域:
- 数字健康数字健康
- 可穿戴技术是可穿戴的技术.
- 机器学习在医学中的应用
背景情况:
- 随着COVID-19的流行,人们越来越需要远程健康监测和早期疾病检测.
- 现有的解决方案需要病原体特定的数据,限制在新型疫情期间使用它们.
- 预训练的模型提供了一个潜在的解决方案,可以在新的传染病情景中快速部署.
研究的目的:
- 调查预训练的临床决策支持算法的适应性,以使用可穿戴生理数据检测早期的COVID-19感染.
- 评估在新的传染病爆发期间部署可穿戴监测系统用于医院外疾病检测的可行性.
主要方法:
- 使用了一个预训练过的算法,最初是为医院获得的感染而设计的.
- 将算法应用于医院外环境中个体的可穿戴生理信号.
- 实施无监督的数据集转移转换,以适应模型的新数据.
主要成果:
- 预训练模型的直接应用使得接收器操作特征曲线 (AUROC) 下的面积为0.52.
- 经过数据调整后,模型性能得到改善,达到0.74.7的AUROC.
- 适应模型在诊断测试前平均2天检测到COVID-19感染.
结论:
- 带有预训练模型的可穿戴监控系统可以很容易地适应用于早期检测医院外疾病.
- 这种方法有助于在新传染病出现时迅速应对.
- 可穿戴技术与可适应的人工智能相结合,对公共卫生监测具有前景.
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