正念的规范性预测者 对于社会焦虑障碍的生态瞬间干预:机器学习对随机控制试验数据的分析
Nur Hani Zainal1, Hui Han Tan1, Ryan Yee Shiun Hong1
1Department of Psychology, National University of Singapore, Singapore, Singapore.
JMIR mental health
|May 13, 2025
概括
机器学习模型有效地识别了患有社会焦虑障碍 (SAD) 的人,他们可能从自我引导的正念干预中受益,优于传统方法. 这有助于量身定制可扩展的治疗方法,以获得更好的结果.
科学领域:
- 精神病学和心理健康 精神病学和心理健康
- 数字健康数字健康
- 医疗保健中的机器学习
背景情况:
- 社会焦虑障碍 (SAD) 的治疗受到耻辱和成本的阻碍,需要可扩展的,简短的干预措施.
- 预测模型对于优化SAD治疗分配至关重要.
- 经典回归可能无法完全捕捉复杂的患者治疗相互作用.
研究的目的:
- 应用精准医学和机器学习 (ML) 来识别自我引导正念干预 (MEMI) 与自我监控 (SM) 成功优化的预测因素.
主要方法:
- 191名可能患有SAD的参与者被随机分配到MEMI或SM.
- 机器学习模型 (随机森林,支持矢量机器) 使用17个基线预测器进行训练.
- 优化被定义为在治疗后和1个月的随访期间,SAD缓解的可能性更高.
主要成果:
- 机器学习模型的性能明显优于后勤回归,达到AU-ROC值为0.71.72.
- 优化MEMI的关键预测因素包括较高的特征正念,较低的SAD严重程度,大学教育,没有心理药物,更高的泛性焦虑,诊断的抑郁/焦虑,以及中国族裔.
- 这些预测因素始终确定了具有更高缓解概率的个体.
结论:
- ML模型在确定SAD治疗优化规范性预测因子方面表现出中等的有效性.
- 识别客户的优点,弱点和种族可以改善对可扩展治疗的反应预测.
- 一个"处方预测计算器"可以指导资源分配和SAD的分层护理,可能使用MEMI作为预密治疗选择.
关键词:
焦虑是一种焦虑.抑郁症严重程度 抑郁症的严重程度数字心理健康数字心理健康数字心理健康干预数字心理健康干预生态生态生态的生态.生态的瞬间干预.干预干预干预干预干预干预在纵向的长度上.机器学习是机器学习.保持正念,就是正念.手机电话 手机电话手机电话精准医学是一门精准医学.预测器 预测器 预测器随机对照试验是随机对照试验.社会焦虑障碍 社会焦虑障碍更多相关视频
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