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在韩国老年人中使用机器学习算法预测晚发性抑郁症

Jong Wan Park^1,2, Chang Woo Ko², Diane Youngmi Lee³

¹Department of Counseling, Graduate School of Hannam University, 70 Hannam-ro, Daedeok- gu, Daejeon, 34430, South Korea.

Scientific reports

|January 8, 2025

概括

这项研究开发了机器学习模型,以预测老年人晚发性抑郁症 (LOD). 随机森林和渐变增强算法显示出强大的预测性能,为新的诊断工具铺平了道路.

科学领域:

老年学是指老年学的学科.
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:

晚发性抑郁症 (LOD) 是老年人群的一个重大问题.
早期预测LOD对于预防严重抑郁症发作至关重要.
识别有风险的个体可以改善临床结果和生活质量.

研究的目的:

开发和验证机器学习 (ML) 模型,用于预测未来晚发抑郁症.
在韩国老年人群中确定LOD的关键预测因素.
为临床决策支持建立基于ML的新型预测程序.

主要方法:

利用了来自"韩国老化纵向研究" (KLOSA) 全国面板调查的数据.
使用潜增长建模和增长混合建模来定义抑郁轨迹.
应用二进制物流回归来选择预测变量并测试了12个ML算法.

主要成果:

确定了老年人抑郁轨迹的四个不同的潜在类别.
选择了12个显著的变量,使LOD与非LOD群体区分开来.
随机森林分类器和梯度提升分类器表现出卓越的预测准确性.

结论:

成功开发了基于ML的晚期抑郁症预测程序.
这些模型表明,在临床环境中早期检测LOD具有很高的潜力.
未来的发展可能会导致对初级保健和健康查进行自我检查的在线工具.

关键词:

抑郁的轨迹抑郁的轨迹晚期开始的抑郁症是晚期开始的.长度研究衰老的研究.机器学习算法机器学习算法预测性表现的预测性表现.

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