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开发和验证机器学习模型,以预测认知障碍的老年人抑郁症状的风险

Enguang Li¹, Fangzhu Ai¹, Qingyan Tian²

¹School of Nursing, Jinzhou Medical University, Liaoning Province, Jinzhou, 121000, China.

|March 12, 2025

概括

机器学习模型,特别是XGBoost,可以有效地预测认知障碍老年人的抑郁症状. 这种方法有助于早期检测和干预,改善这个脆弱人群的健康结果.

科学领域:

老年学是一门学科.
计算精神病学是一种计算精神病学.
流行病学流行病学

背景情况:

认知障碍和抑郁症状在老年人中很常见,并且存在联系.
目前在这个群体中检测抑郁症的方法通常是不够的.
机器学习为早期识别和干预提供了一个强大的工具.

研究的目的:

开发和评估用于预测认知障碍老年人抑郁症状的机器学习模型.
确定这一群体中抑郁症状的关键预测因素.
为此预测任务评估各种机器学习算法的性能.

主要方法:

利用了来自945名60岁以上认知障碍参与者的数据 (NHANES 2011-2014).
使用患者健康问卷-9.9评估抑郁症状.
采用拉索回归来选择特征,并训练/评估XGBoost,物流回归,随机森林和SVM模型.

主要成果:

14.07%的认知障碍老年人表现出抑郁症状.
关键预测因素包括一般健康状况,记忆困难和年龄;一般健康状况是一个新的重要预测因素.
在区分,校准和临床实用性方面,XGBoost表现出卓越的性能.

结论:

机器学习模型,特别是XGBoost,可以准确地预测认知障碍老年人的抑郁症状.
身体,认知和社会因素对于评估抑郁风险至关重要.
这些模型可以增强早期查和干预,改善患者的治疗结果.

关键词:

认知障碍认知障碍是一种认知障碍.抑郁症的症状抑郁症的症状机器学习机器学习尼汉斯 (NHANES) 是一个名人.年龄较大的成年人.

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