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基于社会生态系统理论的可解释机器学习模型和在线网络计算器的开发和验证,用于早期预测产后抑郁症:一项纵向研究

Shusen Lin¹, Peng Wang², Chongyu Yue³

¹School of Nursing, Qingdao University, Qingdao, Shandong, China.

Frontiers in public health

|November 3, 2025

概括

这项研究开发了一种随机森林模型,以早期预测产后抑郁症 (PPD) 风险. 这种可解释的工具可以帮助医疗保健提供者及时进行干预,以预防PPD.

科学领域:

医疗信息学医疗信息学
公共卫生公共卫生
医疗保健中的机器学习

背景情况:

产后抑郁症 (PPD) 是一个重要的全球健康问题,影响到母亲和家庭.
在临床环境中,急需准确的PPD早期风险预测模型.

研究的目的:

开发和验证可解释的机器学习模型,用于早期预测产后抑郁风险.
创建一个临床查工具,用于识别患有PPD高风险的母亲.

主要方法:

以社会生态系统理论为指导,收集多维风险因素的纵向研究.
利用了10个机器学习算法,包括随机森林,以及用于模型解释性的SHAP.
使用个人预后或诊断多变量预测模型 (TRIPOD) 检查清单的透明报告和风险分层的K-Means集群的验证模型.

主要成果:

随机森林模型表现出优异的性能,AUC为0.91 (内部) 和0.77 (外部) 验证.
风险分层确定了低 (0-0.26),中等 (0.26-0.63) 和高 (0.63-1) PPD风险概率.
开发了一个可解释的模型和一个在线风险预测计算器.

结论:

成功开发了一种可解释的早期PPD风险预测模型.
这种工具可以帮助医疗保健提供者早期识别和干预PPD预防.
这项研究为改善母亲心理健康结果提供了宝贵的资源.

关键词:

集群算法集群算法集群算法集群算法集群算法机器学习是机器学习.产后抑郁症产后抑郁症预测性学习模型的预测性学习模型风险评估风险评估风险评估

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