Abstract

Objective: We aimed to develop and test an algorithm for individual patient predictions of problem coping experiences (PCE) (i.e., patients’ understanding and ability to deal with their problems) effects in cognitive–behavioral therapy. Method: In an outpatient sample with a variety of diagnoses (n=1010), we conducted Dynamic Structural Equation Modelling to estimate within-patient cross-lagged PCE effects on outcome during the first ten sessions. In a randomly selected training sample (2/3 of the cases), we tried different machine learning algorithms (i.e., ridge regression, LASSO, elastic net, and random forest) to predict PCE effects (i.e., the degree to which PCE was a time-lagged predictor of symptoms), using baseline demographic, diagnostic, and clinically-relevant patient features. Then, we validated the best algorithm on a test sample (1/3 of the cases). Results: The random forest algorithm performed best, explaining 14.7% of PCE effects variance in the training set. The results remained stable in the test set, explaining 15.4% of PCE effects variance. Conclusions: The results show the suitability to perform individual predictions of process effects, based on patients’ initial information. If the results are replicated, the algorithm might have the potential to be implemented in clinical practice by integrating it into monitoring and therapist feedback systems.

摘要

目的：我们旨在开发和测试一种算法，用于个体患者预测问题应对经验 (PCE)（即患者的理解和处理问题的能力）在认知行为治疗中的影响。方法：在具有多种诊断的门诊样本中（n= 1010），我们进行了动态结构方程建模，以估计前十个疗程中患者内交叉滞后 PCE 对结果的影响。在随机选择的训练样本（2/3 的案例）中，我们尝试了不同的机器学习算法（即岭回归、LASSO、弹性网络和随机森林）来预测 PCE 效应（即 PCE 在多大程度上是症状的时间滞后预测因子），使用基线人口统计、诊断和临床相关的患者特征。然后，我们在测试样本（1/3 的案例）上验证了最佳算法。结果：随机森林算法表现最好，解释了训练集中 14.7% 的 PCE 效应方差。结果在测试集中保持稳定，解释了 15.4% 的 PCE 效应方差。结论：结果显示了基于患者的初始信息对过程效果进行个体预测的适用性。如果结果被复制，该算法可能有可能通过将其集成到监测和治疗师反馈系统中而在临床实践中实施。