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使用机器学习来预测中国中年和老年人口的抑郁症,并进行经验分析

Zhe Wang¹, Ni Jia²

¹Department of Public Health, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China.

|March 18, 2025

概括

一个机器学习模型有效地识别了45岁及以上的中国成年人的抑郁症. 关键预测因素包括生活满意度,自我评估健康,疼痛,睡眠和认知功能,有助于早期检测.

科学领域:

老年学是一门学科.
精神病学是一个精神病学.
计算医学是一种计算医学.

背景情况:

抑郁症在中年和老年人群中构成了重大的公共卫生挑战.
准确和可访问的查工具对于早期干预和管理至关重要.

研究的目的:

开发和验证基于机器学习的抑郁症预测模型,用于45岁及以上的中国成年人.
在这个人口群体中确定导致抑郁症状的关键因素.

主要方法:

利用2020年中国健康和退休调查 (CHARLS) 的数据进行模型开发.
采用了五种机器学习算法,包括一个堆叠组合模型,在CHARLS队列上进行训练和验证.
通过在中国西省进行的单独调查,对该模型进行了外部验证.
应用了夏普利添加解释 (SHAP) 来确定预测因素的重要性.

主要成果:

堆叠组合模型在训练队列的测试组中实现了0.8021的曲线下的面积 (AUC).
该模型表现出强大的外部验证性能,AUC为0.7448.
堆叠组合模型的表现优于单个基础机器学习算法.

结论:

堆叠组合模型是中年和老年中国人的大规模抑郁症查的强大而有效的工具.
抑郁症的重要预测因素包括生活满意度,自我报告的健康状况,疼痛,睡眠时间和认知功能.

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