ABSTRACT

Objective

The aim of this proof-of-concept study is to develop a predictive model based on deep learning algorithms to predict working alliances after the first therapeutic session and to provide a basis for clinical decisions.

Methods

Using a sample of 325 patients and 32 psychotherapists from three university counseling centers, a deep learning algorithm known as fully connected neural networks (FCNNs) was adopted to construct data-driven predictive models. The performance differences between the model including only patient indicators and the model including both patient and therapist indicators were compared. The optimal model was further tested in a general hospital sample of 85 patients and 8 therapists.

Results

The model incorporating both patient indicators and therapist-level indicators (R²: 0.30 ± 0.02) performed better than the model incorporating only patient indicators (R²: 0.11 ± 0.02). The performance of this model decreased when being transferred to the independent general hospital sample, but still retained some predictive value (R² = 0.11).

Conclusion

This study showed that the inclusion of therapist-level indicators can improve the performance of a predictive model in predicting working alliances. This model could assist clinical decisions on choosing psychotherapists for patients and may also initiate new possibilities for future research.

中文翻译：

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使用深度学习算法预测第一次会议工作联盟：个性化心理治疗的概念验证研究

摘要

客观的

这项概念验证研究的目的是开发一个基于深度学习算法的预测模型，以预测第一次治疗后的工作联盟，并为临床决策提供基础。

方法

使用来自三个大学咨询中心的 325 名患者和 32 名心理治疗师作为样本，采用称为全连接神经网络 (FCNN) 的深度学习算法来构建数据驱动的预测模型。比较了仅包括患者指标的模型与包括患者和治疗师指标的模型之间的性能差异。最佳模型在 85 名患者和 8 名治疗师的综合医院样本中进行了进一步测试。

结果

包含患者指标和治疗师水平指标（R²：0.30 ± 0.02）的模型比仅包含患者指标的模型（R²：0.11 ± 0.02）表现更好。当转移到独立的综合医院样本时，该模型的性能有所下降，但仍保留了一些预测值 (R² = 0.11)。

结论

这项研究表明，包含治疗师级别的指标可以提高预测模型在预测工作联盟方面的表现。该模型可以帮助为患者选择心理治疗师的临床决策，也可以为未来的研究开启新的可能性。