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预测青少年对基于学校的正念的反应:MYRIAD试验的二次分析

Christian A Webb^1,2, Boyu Ren^1,2, Verena Hinze³

¹Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts.

JAMA psychiatry

|February 18, 2026

概括

机器学习确定了一小部分青少年可能受益于基于学校的正念训练 (SBMT). 然而,检测到的有效性差异太小,无法在临床上对个性化预防策略有用.

科学领域:

青少年心理健康青少年心理健康
预防性精神病学是一种预防性精神病学.
机器学习在医疗保健中的应用

背景情况:

青少年抑郁症需要早期预防,以学校为基础的正念训练 (SBMT) 为可扩展的方法.
目前关于SBMT有效性的证据是混合的,表明需要个性化预防策略.
预测哪些青少年最受益于SBMT对于优化干预结果至关重要.

研究的目的:

开发一个基于数据的算法,使用基线特征来预测哪些青少年最有可能从SBMT中受益.
为青少年心理健康提供基于学校的预防性干预措施提供个性化服务.

主要方法:

青春期我的性 (MYRIAD) 集群随机临床试验的二次分析 (ISRCTN86619085).
利用学校级嵌套交叉验证来训练和评估机器学习模型 (因果森林和弹性净回归).
评估SBMT对标准社会情绪学习的个性化益处,基于抑郁症状的变化.

主要成果:

机器学习模型确定了青少年的一个子集,预计从SBMT中受益,症状严重程度和学校因素是关键预测因素.
因果森林显示了可接受的校准,而弹性净回归显示了适度的预测性能.
预测响应者和非响应者之间结果的群体差异在统计上是可检测的,但在临床上是可以忽略不计的.

结论:

机器学习确定了具有统计学上显著但临床上微不足道的SBMT差异反应的子组.
在基于学校的普遍预防计划中实现临床上有意义的个性化提出了重大挑战.
需要进一步的研究来完善青少年心理健康干预的个性化方法.

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