使用机器学习算法来预测心理治疗中变化过程的影响:朝着过程级别的治疗个性化
Juan Martín Gómez Penedo1, Julian Rubel2, Manuel Meglio1
1Facultad de Psicologia, Universidad de Buenos Aires.
Psychotherapy (Chicago, Ill.)
|October 5, 2023
概括
机器学习算法可以预测心理治疗变化过程的个人相关性,例如对治疗结果的掌握和澄清. 这些算法显示出指导个性化治疗建议的前景.
科学领域:
- 心理学 心理学 心理学
- 心理治疗研究 心理治疗研究
- 医疗保健中的机器学习
背景情况:
- 个性化医疗在心理治疗中越来越重要.
- 确定哪些治疗变化过程对个体患者最相关,可以优化治疗.
- 掌握和澄清是心理治疗的两个关键变化过程.
研究的目的:
- 开发和验证机器学习算法,用于预测心理治疗中掌握和澄清过程的个体相关性.
- 评估使用这些算法来指导治疗建议的可行性.
主要方法:
- 在最初的10次会议中,研究了接受综合治疗的自然主义门诊样本 (n=608).
- 估计了治疗师感知到的掌握和对随后的患者评估结果的澄清对患者内部的影响.
- 机器学习算法在子样本 (n=407) 上进行训练,以使用基线特征预测过程相关性,并在持久子样本 (n=201) 上进行测试.
主要成果:
- 观察到对结果的掌握和澄清有显著的患者内效应.
- 预测算法显示,在持久样本中,掌握 (r=.18) 和澄清 (r=.16) 的相关性小到中.
- 算法识别了指导 (14%) 或澄清 (18%) 的患者,指导焦点与指导组中更好的结果相关 (r=.33).
结论:
- 使用机器学习,可以对心理治疗过程相关性的个体预测.
- 这些发现支持了算法对治疗师反和个性化治疗建议的潜在实用性.
- 需要与潜在的实验设计进行复制以确认这些结果.
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