在使用分类树的群体认知行为疗法 (CBT) 中改善了学预测
Ashleigh G Cameron1, Andrew C Page1, Geoff R Hooke2
1School of Psychological Sciences, The University of Western Australia, Perth, Australia.
概括
在认知行为疗法 (CBT) 中,可以使用分类树预测患者的退学情况. 伴随性疾病的诊断是关键预测因素,密集的CBT显示出较低的学率.
科学领域:
- 精神病学是一个精神病学.
- 临床心理学 临床心理学
- 在医疗保健中的数据科学.
背景情况:
- 心理治疗中断显著降低了治疗的有效性.
- 预测患者退学仍然是临床实践中的一个挑战.
- 分类树提供了一种实际的方法,可以使用摄入数据来识别有风险的患者.
研究的目的:
- 开发和测试分类树模型,用于预测每周和强化认知行为疗法 (CBT) 群体课程中的学.
- 为了确定与患者退学相关的关键摄入变量.
主要方法:
- 在每周和密集性CBT计划 (2015-2019) 中从白天患者收集的摄入数据.
- 训练并测试了两个分类树模型来预测学.
- 分析了各种摄入变量的预测能力,重点关注并发症.
主要成果:
- 每周CBT的学率为21.9%,密集CBT的学率为13.2%.
- 伴随性疾病诊断的数量是两项计划中学最重要的预测因素.
- 分类树模型实现了适度的预测准确性 (约为62%-63%),用于识别放弃者.
结论:
- 伴随性疾病是评估CBT患者学风险的关键因素.
- 简单的分类树模型可以在治疗早期预测中等准确度的学.
- 密集的,紧缩的治疗格式可能会改善患者的留守率.
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