开发可解释的基于机器学习的预测模型,用于中老年和老年中国人中轻度和重度失眠的抑郁症
Ziyan Wang1, Yuqin Zhou2, Songbowen Qi3
1School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, 210023, China; Institute of Chinese Medicine Literature, Nanjing University of Chinese Medicine, Nanjing, 210023, China.
Journal of affective disorders
|November 12, 2025
概括
这项研究开发了人工智能模型来预测失眠的老年人抑郁风险,区分轻度和重度病例. 这些模型提供个性化的风险评分和解释,以指导有针对性的预防策略.
科学领域:
- 老年学是指老年学的学科.
- 睡眠医学 睡眠医学
- 医疗保健中的人工智能
背景情况:
- 失眠的老年人患抑郁症的风险更高,但轻度和重度的病例有不同的途径.
- 当前的工具忽视了失眠的异质性,将其视为一个单一的问题.
- 需要可解释的,严重程度特定的模型来预测抑郁风险和指导预防.
研究的目的:
- 创建一个可解释的,严重程度特定的AI框架,用于量化中年和老年人的抑郁风险.
- 确定与失眠相关的抑郁风险的共同和严重程度特定的决定因素.
- 引导针对睡眠障碍老年人群中抑郁症的有针对性的预防工作.
主要方法:
- 来自中国健康与退休长度研究的2433名参与者 (≥45岁) 的分析.
- 失眠的分类是轻度 (5-6小时) 和重度 (<5小时) 的睡眠时间.
- 机器学习算法的应用 (随机森林被选为最佳) 与SHapley添加式扩展 (SHAP) 进行解释性.
主要成果:
- 随机森林模型实现了高性能 (轻度AUC为0.7784,重度失眠为0.7554) 和良好的校准.
- 关键的危险因素包括年龄较大,女性性别,自我评估健康状况不佳和多病症.
- 发现的保护因素包括生活满意度,功能独立性,认知储备和教育.
结论:
- 同时,可解释的随机森林模型根据失眠的严重程度对抑郁症风险进行分层,突出普遍和特定的驱动因素.
- 一个基于Web的工具与SHAP可视化提供实时风险分数和解释.
- 该框架支持在初级保健中进行分层查和量身定制的咨询,推进精确的抑郁症预防.
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