使用基于机器学习的简单问卷预测睡眠障碍风险:开发和验证研究
Seokmin Ha1,2, Su Jung Choi3, Sujin Lee4
1Biomedical Mathematics Group, Institute for Basic Science, Daejeon, Republic of Korea.
Journal of medical Internet research
|September 21, 2023
概括
一个新的机器学习工具SLEEPS准确地预测了阻塞性睡眠呼吸暂停 (OSA),并发性失眠和睡眠呼吸暂停 (COMISA) 和使用简单问卷的失眠风险,提高了诊断的可访问性.
科学领域:
- 睡眠医学 睡眠医学
- 医疗保健中的人工智能
- 诊断工具 诊断工具
背景情况:
- 睡眠障碍如阻塞性睡眠呼吸暂停 (OSA),并发性失眠和睡眠暂停 (COMISA) 和失眠是普遍存在的,并对健康构成重大风险.
- 准确的诊断受到认知不足,时间密集的睡眠监测和患者不愿意接受多睡眠记录的阻碍.
研究的目的:
- 开发一种机器学习算法,通过简单的问卷来预测OSA,COMISA和失眠风险.
- 在初始睡眠障碍风险评估中消除了对多睡眠图的需求.
主要方法:
- 对来自两个医疗中心的数据 (n=4257和n=365) 进行了极端梯度增强.
- 特性选择使用了沙普利增量解释 (SHAP) 来获得最佳的预测能力.
- 一个基于问卷的算法,SLEEPS,是使用接收器操作特性曲线下的面积来开发和验证的.
主要成果:
- 在SLEEPS问卷调查中发现了9个关键特征.
- 在数据集中,SLEEPS对所有三种睡眠障碍都表现出高准确度 (AUC > 0.897).
- 区分COMISA和OSA对于预测准确性至关重要.
结论:
- SLEEPS为睡眠障碍风险评估提供了一种更容易获得和更方便的方法.
- 基于SLEEPS的公开可访问的网站提供了一个易于使用的风险预测和管理工具.
- 该工具有可能显著改善睡眠障碍诊断和治疗的可访问性.
关键词:
在XGBoost中使用.同病性失眠和睡眠呼吸暂停.失眠是因为失眠.机器学习是机器学习.阻塞性睡眠呼吸暂停 (SOP) 的情况.聚类人体图像 (polysomnography) 是一种多人体图像.问卷,问卷,问卷,问卷,问卷.危险的风险 危险的风险风险预测风险预测睡眠 睡眠 睡眠 睡眠 睡眠更多相关视频
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