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在患有慢性阻塞性肺病的患者中开发和验证抑郁症风险预测模型

Tong Feng^1,2, PeiPei Li^3,4, Ran Duan^3,5

¹Department of Respiratory and Critical Care Medicine, Deyang People's Hospital, Affiliated Hospital of Chengdu College of Medicine, Deyang, China.

|July 3, 2025

概括

一种新的机器学习模型使用临床数据准确预测慢性阻塞性肺病 (COPD) 患者的抑郁风险. 这种工具有助于早期干预,以获得更好的心理健康结果.

科学领域:

呼吸系统医学呼吸系统医学
精神病学是一个精神病学.
数据科学数据科学数据科学

背景情况:

慢性阻塞性肺病 (COPD) 经常与抑郁症同时发生,使患者的结果和生活质量恶化.
在COPD患者中早期发现抑郁风险是一个重大的临床挑战.

研究的目的:

开发和验证用于预测COPD患者抑郁风险的机器学习模型.
在早期干预策略中利用可解释的临床和人口特征.

主要方法:

来自国家健康和营养检查调查 (NHANES) 的1638名COPD患者的分析.
使用患者健康问卷-9 (PHQ-9) 评估抑郁症.
通过Boruta和LASSO算法进行特征选择;使用AUC,精度和校准训练和评估九个机器学习模型.

主要成果:

确定了关键预测因素:睡眠障碍,年龄,贫困,高血压和心血管疾病.
支持矢量机 (SVM) 模型表现出高性能 (AUC ~ 0.89),具有强大的区分能力.
沙普利添加式扩张 (SHAP) 分析证实了可解释性;睡眠障碍,年轻年龄和社会经济贫困与更高的抑郁风险有关.

结论:

一个可靠的基于SVM的模型有效地预测了COPD患者使用可解释特征的抑郁风险.
该模型是早期查和个性化护理的宝贵工具,有可能改善COPD患者的心理健康.

关键词:

慢性阻塞性肺病 (COPD) 是一种慢性阻塞性肺病.抑郁症抑郁症抑郁症机器学习机器学习国家健康和营养检查调查 (NHANES)预测模型的预测模型

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