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引用次数: 0
摘要
背景:流失是临床实践和研究中的一个重要问题。然而,人们对认知行为疗法(CBT)治疗惊恐障碍(PD)的辍学预测因素并不完全了解。在本研究中,我们旨在利用机器学习(ML)算法建立一个针对惊恐障碍认知行为疗法的辍学预测模型:我们对 208 名 PD 患者进行了集体 CBT 治疗。根据基线数据,使用随机森林和轻梯度提升机两种ML算法进行预测分析。基线数据包括NEO五因素指数的五个人格维度、症状检查表-90修订版的抑郁分量表、年龄、性别和恐慌症严重程度量表:结果:随机森林识别出了在针对帕金森病的 CBT 治疗过程中出现的辍学现象,预测准确率为 88%。光梯度提升机的预测准确率为 85%:结论:ML算法能以相对较高的准确率检测出帕金森病CBT治疗后的辍学情况。在临床决策中,我们可以使用这种 ML 方法。本研究是在常规临床环境中进行的自然研究。因此,我们的 ML 方法结果可以推广到常规临床环境中。
Predicting Dropout From Cognitive Behavioral Therapy for Panic Disorder Using Machine Learning Algorithms.
Background: Attrition is an important problem in clinical practice and research. However, the predictors of dropping out from cognitive behavioral therapy (CBT) for panic disorder (PD) are not fully understood. In this study, we aimed to build a dropout prediction model for CBT for PD using machine learning (ML) algorithms.
Methods: We treated 208 patients with PD applying group CBT. From baseline data, the prediction analysis was carried out using two ML algorithms, random forest and light gradient boosting machine. The baseline data included five personality dimensions in NEO Five Factor Index, depression subscale of Symptom Checklist-90 Revised, age, sex, and Panic Disorder Severity Scale.
Results: Random forest identified dropout during CBT for PD showing that the accuracy of prediction was 88%. Light gradient boosting machine showed that the accuracy was 85%.
Conclusions: The ML algorithms could detect dropout after CBT for PD with relatively high accuracy. For the purpose of clinical decision-making, we could use this ML method. This study was conducted as a naturalistic study in a routine clinical setting. Therefore, our results in ML approach could be generalized to regular clinical settings.