基于机器学习的睡眠质量风险预测模型的构建和验证,用于OSA患者的睡眠质量
Yangyang Tong1, Kuo Wen2, Enguang Li3
1Department of Pulmonary Oncology, Affiliated Hospital of Changchun University of Traditional Chinese Medicine, Changchun, Jilin, 130117, People's Republic of China.
Nature and science of sleep
|June 18, 2025
概括
机器学习准确地预测了阻塞性睡眠呼吸暂停 (OSA) 患者的睡眠质量. 抑郁症状和氧气不和指数 (ODI) 显著影响睡眠质量,有助于临床预测.
科学领域:
- 医疗信息学 医疗信息学
- 睡眠医学 睡眠医学
- 机器学习 机器学习
背景情况:
- 阻塞性睡眠呼吸暂停 (OSA) 显著影响患者的生活质量.
- 预测和理解影响OSA睡眠质量的因素对于有效管理至关重要.
研究的目的:
- 开发一种高性能机器学习模型,用于预测OSA患者的睡眠质量.
- 确定在OSA人群中睡眠质量的关键预测因素.
主要方法:
- 开发了一个LightGBM模型,并与其他机器学习算法进行验证.
- 使用AUC,校准曲线和决策曲线分析 (DCA) 来评估性能.
- 为了模型的解释性和预测器的识别,使用了夏普利添加式解释 (SHAP).
主要成果:
- 轻GBM型号实现了0.910的AUC,优于其他型号.
- 确定的主要预测因素包括抑郁症状,OSA持续时间,ODI,焦虑症状,运动频率和咖啡消费.
- SHAP分析强调抑郁症状和ODI是对睡眠质量的主要负面影响.
结论:
- 轻GBM模型提供了一个强大的工具来预测OSA患者的睡眠质量.
- 了解已识别的预测因素可以为目标干预提供信息.
- 这种预测模型可以帮助临床医生改善患者的预后和生活质量.
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