预测抑郁症并揭开其在中年和老年人群中的异质影响:一种机器学习方法
Ling Zhang1, Ruigang Wei2, Jingwen Zhou1
1School of Software and Internet of Things, Jiangxi University of Finance and Economics, Nanchang, China.
BMC psychology
|April 17, 2025
概括
机器学习有效地识别了45岁以上中国成年人的抑郁风险因素,包括残疾和生活满意度. 这种混合模型有助于在老年人群中早期发现和干预抑郁症.
科学领域:
- 老年学是指老年学的学科.
- 计算精神病学是一种计算精神病学.
- 公共卫生 公共卫生
背景情况:
- 衰老是一个全球趋势,抑郁症对中老年人和老年人构成重大威胁.
- 现有的抑郁风险因素研究经常使用小规模数据和传统的统计方法.
- 使用机器学习对老年人抑郁症风险因素的大规模数据分析是有限的.
研究的目的:
- 应用机器学习方法,在中国中年和老年人的大规模数据集中识别抑郁症风险因素.
- 探索人口,生活方式,健康和社会经济变量对抑郁症的预测力.
- 在这个人口群体中开发一种有效的早期诊断和抑郁症干预模型.
主要方法:
- 使用了两步混合模型,将长短期记忆 (LSTM) 和机器学习 (ML) 结合起来.
- 分析了中国健康与退休纵向研究 (CHARLS) 的一个平衡面板数据集,涵盖了五个波段 (2011-2020年).
- 评估了20个抑郁风险/保护因素,使用LSTM进行预测和5个ML模型进行抑郁症分类.
主要成果:
- 该LSTM模型证明了与抑郁症相关的变量的有效预测 (MSE = 0.067).
- 在ML模型中,平均AUC从0.78到0.82.82不等.
- 关键预测因素包括残疾,生活满意度,日常生活活动 (ADL) 障碍,慢性疾病和自我报告的记忆.
结论:
- 混合LSTM+ML模型有效地使用人口和健康数据在两年内预测抑郁症.
- 该模型有助于在中年和老年人中早期诊断和干预抑郁症.
- 残疾,生活满意度和慢性疾病被确定为重要的危险因素,中年和老年人之间存在差异.
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