使用商业可穿戴设备早期检测不良生理事件:挑战和机遇
Jesse Phipps1, Bryant Passage1, Kaan Sel2
1Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA.
NPJ digital medicine
|May 23, 2024
概括
商业可穿戴设备和机器学习可以早期检测疾病. 新的算法通过解决数据挑战来提高检测准确性,使COVID-19和其他疾病的症状前识别成为可能.
科学领域:
- 数字健康数字健康
- 生物医学信息学 生物医学信息学
- 机器学习应用 机器学习应用
背景情况:
- 商用现成的可穿戴设备 (COTS) 与机器学习 (ML) 结合,对早期检测不良生理事件充满希望.
- 关键的挑战包括参与者的异质性,混因素,传感器数据噪声和不准确的自我报告标签,限制算法的可扩展性和准确性.
- 解决这些局限性对于实现可穿戴技术在健康监测中的全部潜力至关重要.
研究的目的:
- 描述ML算法在现实数据挑战下对生理事件检测的性能.
- 开发新的算法,减轻数据异质性,噪音和标签不准确等挑战.
- 提供关于利用可穿戴设备推进基于ML的健康监测的局限性和机会的见解.
主要方法:
- 开发结合参与者特定基线确定以捕捉生理变化的算法.
- 实施标签校正技术,以解决自我报告的健康数据中的不准确性.
- 在一个大型数据集上进行验证,包括超过8000名参与者和来自Oura智能戒指的130万小时的数据.
主要成果:
- 在COVID-19的预症状检测中,通过提出的纠正技术,在曲线下 (AUC) 实现了0.777的接收器操作员特征 (ROC) 区域.
- 自报发烧的检测达到0.994的ROC AUC,呼吸短促达到0.635,表明新算法显著改善.
- 该研究表明,改善了COVID-19的早期检测,平均在阳性测试前4.1天,而没有纠正的3.5天.
结论:
- 开发的算法有效地解决了可穿戴传感器数据中的数据挑战,提高了基于ML的生理事件检测的精度.
- 基线和标签校正技术显著提高了用于检测各种健康事件 (包括传染病) 的算法的性能.
- 通过可穿戴设备的持续监测,加上先进的算法,为主动和症状前的健康监测提供了一个强大的工具.
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