微调个性化机器学习模型以检测基于智能床平台数据的失眠风险
Trevor Winger1,2, Vidhya Chellamuthu1, Dmytro Guzenko3
1Sleep Number Labs, Sleep Number, San Jose, CA, United States.
Frontiers in neurology
|February 29, 2024
概括
个性化的机器学习模型使用智能床数据准确地检测失眠风险. 精细调整仅需五次睡眠会话,显著提高了检测准确度,显示了睡眠医学应用的前景.
科学领域:
- 睡眠医学 睡眠医学
- 机器学习 机器学习
- 数字健康数字健康
背景情况:
- 失眠影响全球10-30%,造成不良健康影响.
- 客观的睡眠数据和验证的问卷对准确的诊断至关重要.
研究的目的:
- 调查个性化的机器学习算法在检测失眠风险方面的有效性.
- 评估智能床的纵向睡眠数据对失眠检测准确性的影响.
主要方法:
- 使用Sleep Number智能床平台的1489名参与者的数据.
- 根据失眠严重性指数 (ISI) 值,采用增量,被动攻击性机器学习模型来检测失眠风险.
- 使用个人睡眠会话数据进行个性化模型微调.
主要成果:
- 一个通用模型实现了接收-操作曲线 (AUC) 下的面积大约为0.5.5.
- 个性化微调与五个睡眠会话改善了所有ISI值的AUC超过0.8.
- 超过十个个性化睡眠会话时,没有观察到显著的AUC改善.
结论:
- 个性化的机器学习模型在检测失眠风险方面表现出很高的准确性.
- 有限的个性化数据 (五次会议) 足以显著提高性能.
- 这种方法显示了促进睡眠医学诊断的潜力.
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