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Updated: Sep 13, 2025

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通过可解释机器学习使用血清 perfluoroalkyl 和 polyfluoroalkyl 物质水平预测抑郁症

Hui Jin¹, Yang Wen¹, Shuai Luo²

¹Mental Health Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China; Department of Social Psychiatry, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.

Journal of affective disorders

|August 3, 2025

概括

机器学习模型预测了暴露于per-和多基基物质 (PFAS) 的抑郁风险. 一种特定的PFAS,PFOS的较高水平显示出与抑郁症风险增加相关的值效应.

科学领域:

环境健康环境健康
毒理学毒理学毒理学
计算生物学计算生物学

背景情况:

和多基基物质 (PFAS) 是持久性合成化学物质,人类广泛接触.
之前没有任何研究使用机器学习 (ML) 来根据PFAS暴露预测抑郁风险.
这项研究引入了一个可解释的ML模型,用于评估与血清PFAS水平相关的抑郁风险.

研究的目的:

开发和验证可解释的机器学习模型,用于预测抑郁风险.
为了确定与抑郁症相关的关键PFAS.
根据PFAS暴露,提供一个评估个人抑郁风险的工具.

主要方法:

利用国家健康和营养检查调查 (NHANES) 数据 (2005-2018) 来自9074名参与者.
开发和评估了9个ML模型,选择CatBoost以优异的预测性能 (73%准确率,0.75AUC).
使用部分依赖分析 (PDA) 和夏普利添加式解释 (SHAP) 进行模型解释性和特征分析. 开发了一个网络计算器,用于实时风险评估.

主要成果:

CatBoost模型实现了73%的准确性和0.75.5的AUC.
perfluorooctanesulfonic 酸 (PFOS) 被确定为最有影响力的 PFAS.
观察到PFOS的值效应为11.66 ng/ml,与抑郁症风险升高相关.

结论:

一个可解释的ML模型成功地将PFAS暴露与抑郁风险联系起来.
这些发现支持公共卫生干预和个性化护理策略.
有一个交互式网络计算器可用于将研究结果转化为临床实践.

关键词:

抑郁症抑郁症抑郁症机器学习机器学习尼汉斯 (NHANES) 是一个名人.在PFAS中,有很多方法.预测模型是一个预测模型.

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