纵向可穿戴传感器数据提高了长期COVID检测的精度
Chibuike K Uwakwe1,2, Ekanath Srihari Rangan1,3, Satyajit Kumar1
1Department of Genetics, Stanford University School of Medicine, Stanford University, Stanford, California, United States of America.
PLOS digital health
|November 20, 2025
概括
可穿戴的心率数据可以帮助诊断长期COVID. 使用心率 (HR) 和症状数据的机器学习模型显著提高了长期COVID的诊断准确性,提供了一个非侵入性的工具.
科学领域:
- 生物医学工程 生物医学工程
- 计算生物学 计算生物学
- 传染性疾病 传染性疾病
背景情况:
- 由于缺乏客观生物标志物,长期COVID诊断具有挑战性.
- 这往往导致持续的SARS-CoV-2后症状的不足诊断和误诊.
- 可穿戴传感器数据为客观诊断标记提供了潜在的途径.
研究的目的:
- 调查可穿戴心率 (HR) 传感器数据对于长期COVID诊断的实用性.
- 开发和评估用于预测长期COVID状态的机器学习模型.
- 与基于症状的数据相比,评估HR特征的附加值.
主要方法:
- 分析了来自126名SARS-CoV-2感染后个体的纵向HR数据.
- 机器学习模型使用HR特征 (来自六个类别) 和报告的症状进行训练.
- 模型性能使用接收器操作特征 (ROC) 和精度召回 (PR) 曲线进行评估.
主要成果:
- 使用HR和症状特征的组合模型实现了95.1%的ROC-AUC和85.9%的PR-AUC.
- 这与仅仅症状模型相比,是一个显著的改善.
- 人力资源数据提供了客观特征,增强了诊断歧视.
结论:
- 可穿戴的人力资源数据可以作为长期COVID诊断的客观生物标志物.
- 整合HR数据与症状信息可以提高诊断精度.
- 这种非侵入性方法可以减少错误分类并改善患者的治疗结果.
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