分析抑郁症的患病率及其影响因素在患有阻塞性睡眠呼吸暂停的老年患者:一种机器学习方法
Shuhong Qin1, Zhanhang Zheng1, Ruilin Li1
1Guangxi University of Chinese Medicine, Nanning, Guangxi, China.
Ear, nose, & throat journal
|August 28, 2024
概括
在患有阻塞性睡眠呼吸暂停 (OSA) 的老年患者中,抑郁症状很常见. 机器学习确定了一般健康状况,睡眠障碍和性别是影响该人口心理健康的关键因素.
科学领域:
- 老年学是一门学科.
- 睡眠医学 睡眠医学
- 精神病学是一个精神病学.
背景情况:
- 抑郁症状在患有阻塞性睡眠呼吸暂停 (OSA) 的老年患者中是一个重大问题.
- 在这种人口群体中确定导致抑郁症的因素对于有效的干预至关重要.
- 机器学习提供了一种强大的方法来分析复杂的健康数据并识别关键预测因素.
研究的目的:
- 利用机器学习算法来识别与OSA老年患者抑郁症状相关的因素.
- 开发和比较各种机器学习模型的性能来分类抑郁症状.
- 根据其在预测抑郁症状中的重要性来对已识别的因素进行排名.
主要方法:
- 分析了来自国家健康和营养检查调查 (NHANES) 的数据.
- 包括符合OSA诊断评估条件的老年患者.
- 后勤回归选了初始因素,其次是随机森林 (RF),极端梯度增强 (XGB),人工神经网络 (ANN) 和支持矢量机器 (SVM) 模型用于分类和特征重要性分析.
主要成果:
- 在1538名老年OSA患者中,528人 (34.4%) 报告有抑郁症状.
- 支向量机 (SVM) 模型实现了最高的性能,曲线下的面积 (AUC) 为0.746.
- 影响抑郁症状的关键因素包括一般健康状况,睡眠障碍,性别,尿失禁,肝病,身体活动限制,教育,水分,红细胞分布宽度和听力损失.
结论:
- 患有OSA的老年患者表现出抑郁症状的高患病率.
- 客观和主观因素的结合导致了这一群体的抑郁症.
- 需要紧急关注和有针对性的干预措施,以解决高风险老年OSA患者的心理健康问题.
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