PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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%.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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%.
UNASSIGNED: 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.
摘要：
■损耗是临床实践和研究中的一个重要问题。然而,对于因惊恐障碍(PD)而退出认知行为疗法(CBT)的预测因素尚不完全清楚.在这项研究中,我们的目标是使用机器学习(ML)算法构建PD的CBT下降预测模型。
■我们应用CBT组治疗了208例PD患者。从基线数据来看，使用两种ML算法进行了预测分析，随机森林和光梯度升压机。基线数据包括NEO五因素指数中的五个人格维度，抑郁症子量表的症状清单-90修订，年龄,性别,和恐慌症严重程度量表。
■随机森林在PD的CBT期间识别出脱落，表明预测的准确性为88％。光梯度增强机显示精度为85%。
■ML算法可以以相对较高的精度检测PD的CBT后的丢失。为了临床决策的目的，我们可以使用这个ML方法。这项研究是在常规临床环境中作为自然研究进行的。因此,我们在ML方法中的结果可以推广到常规的临床设置.
■我们应用CBT组治疗了208例PD患者。从基线数据来看，使用两种ML算法进行了预测分析，随机森林和光梯度升压机。基线数据包括NEO五因素指数中的五个人格维度，抑郁症子量表的症状清单-90修订，年龄,性别,和恐慌症严重程度量表。
■随机森林在PD的CBT期间识别出脱落，表明预测的准确性为88％。光梯度增强机显示精度为85%。
■ML算法可以以相对较高的精度检测PD的CBT后的丢失。为了临床决策的目的，我们可以使用这个ML方法。这项研究是在常规临床环境中作为自然研究进行的。因此,我们在ML方法中的结果可以推广到常规的临床设置.
