一个多层次的机器学习算法,用于预测接受认知行为治疗的患者每次会议的结果

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Juan Martín Gómez Penedo¹, Alice E Coyne², Manuel Meglio³

¹Clinical Psychology and Psychotherapy Department, Universität Osnabrück, Lise-Meitner-Str. 3, 49076, Osnabrück, Germany; Clinical Psychology and Psychotherapy Department, Universität Kassel, Kassel, Germany.

Behaviour research and therapy

|September 13, 2025

概括

机器学习模型可以更准确地预测心理治疗的结果. 一种新的基于树的方法,集成多层次和机器学习模型,显示了个性化心理健康护理的前景.

科学领域:

精神病学和心理健康精神病学和心理健康
数据科学和机器学习
临床心理学临床心理学

背景情况:

基于测量的护理 (MBC) 系统可以通过先进的预测模型来改进.
机器学习 (ML) 提供了更准确的可能性,会话对会话心理治疗结果的预测.
将ML与现有的统计模型集成,可以提高MBC的有效性.

研究的目的:

开发和评估一种结合多层次和ML方法的基于树的新型模型.
预测患者在认知行为疗法 (CBT) 中的临床改善轨迹.
通过改善治疗计划,提高心理健康护理的准确性.

主要方法:

使用了来自德国CBT大学诊所的1008名门诊患者的数据集.
采用了通用的线性混合模型树算法进行预测.
随机将样本分为培训 (2/3) 和测试 (1/3) 组,用于模型开发和验证.

主要成果:

最好的模型根据基线特征和改善确定了10个患者组.
该算法在测试组中观察到的和预测的结果之间实现了0.65的相关性 (交叉验证R2 = 0.42).
失败边界正确识别了67.6%的患者,在15次会议内没有可靠的改善.

结论:

初步证据支持使用通用线性混合模型树集成多层次和ML模型.
开发的算法可以帮助精确心理健康护理的常规实施.
这些工具可以为治疗师的治疗计划和对患者需求的响应提供信息.

关键词:

认知行为疗法是一种认知行为疗法.纵向的纵向的纵向的机器学习是机器学习.多层次模型是多层次模型.结果结果的结果.预测模型的预测模型.心理治疗心理治疗

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