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开发和验证可解释的机器学习模型,用于预测老年人多病症睡眠障碍的风险:横截面研究

Xia Wang¹, Dan Zhang², Liu Lu³

¹School of Basic Medical Sciences and School of Nursing, Chengdu University, Chengdu, China.

Frontiers in public health

|August 27, 2025

概括

这项研究开发了一种可解释的AI模型,用于预测多种健康状况的老年人睡眠障碍. 确定的主要危险因素包括虚弱,认知和营养状况以及心理健康.

科学领域:

老年学
人工智能
睡眠医学

背景情况:

睡眠障碍在老年人中普遍存在,尤其是那些多病症患者.
准确的风险预测对于及时干预至关重要.

研究的目的:

开发和验证可解释的机器学习模型,用于预测多病症老年人的睡眠障碍风险.
确定这一群体中睡眠障碍的关键预测因素.

主要方法:

评估了六种机器学习模型:逻辑回归,神经网络,支持向量机,梯度增强机,K-Nearest Neighbors和光梯度增强机.
梯度提升机 (GBM) 模型表现出卓越的性能,实现曲线下的面积 (AUC) 为0.881.
可解释的人工智能技术,特别是SHAP (Shapley添加式解释),用于模型解释性.

主要成果:

在研究队列中睡眠障碍的患病率为28. 7%.
GBM模型实现了最高的预测准确度 (AUC = 0.881).
主要预测因素包括虚弱,认知状况,营养状况,独自生活,抑郁,吸烟状况和焦虑.

结论:

这项研究提供了第一个可解释的AI模型,用于预测多病症的中国老年人睡眠障碍.
这项研究确定了七个显著的危险因素,增强了对这一群体睡眠障碍病因的理解.
SHAP 方法提高了模型的解释性,帮助临床医生了解预测的逻辑.

关键词:

机器学习多病态性年长的成年人预测模型睡眠障碍

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