Comparing three neural networks to predict depression treatment outcomes in psychological therapies

IF 4.2 2区心理学 Q1 PSYCHOLOGY, CLINICAL

Behaviour Research and Therapy Pub Date : 2025-04-22 DOI:10.1016/j.brat.2025.104752

Brian Schwartz , Julia Giesemann , Jaime Delgadillo , Jana Schaffrath , Miriam I. Hehlmann , Danilo Moggia , Christopher Baumann , Wolfgang Lutz

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引用次数: 0

Abstract

Objective

Artificial neural networks have been used in various fields to solve classification and prediction tasks. However, it is unclear if these may be adequate methods to predict psychological treatment outcomes. This study aimed to evaluate the prognostic accuracy of neural networks using psychological treatment outcomes data.

Method

Three neural network models (TensorFlow, nnet, and monmlp) and a generalised linear regression model were compared in their ability to predict post-treatment remission of depression symptoms in a large naturalistic sample (n = 69,489) of patients accessing low intensity cognitive behavioural therapy. Prognostic accuracy was evaluated using the area under the curve (AUC) in an external cross-validation design.

Results

The AUC of the neural networks in an external test sample ranged from 0.64 to 0.65 and the AUC of the linear regression model was 0.63.

Conclusion

Neural networks can help predict symptom remission in new samples with moderate accuracy, although these models were no more accurate than a simpler inferential statistical linear regression model.

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比较三种神经网络在心理治疗中预测抑郁症治疗结果

目的人工神经网络已广泛应用于各种领域，用于解决分类和预测任务。然而，尚不清楚这些方法是否足以预测心理治疗结果。本研究旨在利用心理治疗结果数据评估神经网络预测预后的准确性。方法比较三种神经网络模型（TensorFlow、nnet和monmlp）和广义线性回归模型对接受低强度认知行为治疗的患者治疗后抑郁症状缓解的预测能力。在外部交叉验证设计中，使用曲线下面积（AUC）评估预后准确性。结果神经网络在外部测试样本中的AUC范围为0.64 ~ 0.65，线性回归模型的AUC为0.63。结论神经网络可以帮助预测新样本的症状缓解，但准确度中等，尽管这些模型并不比简单的推理统计线性回归模型更准确。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Behaviour Research and Therapy PSYCHOLOGY, CLINICAL-

CiteScore

7.50

自引率

7.30%

发文量

148

期刊介绍： The major focus of Behaviour Research and Therapy is an experimental psychopathology approach to understanding emotional and behavioral disorders and their prevention and treatment, using cognitive, behavioral, and psychophysiological (including neural) methods and models. This includes laboratory-based experimental studies with healthy, at risk and subclinical individuals that inform clinical application as well as studies with clinically severe samples. The following types of submissions are encouraged: theoretical reviews of mechanisms that contribute to psychopathology and that offer new treatment targets; tests of novel, mechanistically focused psychological interventions, especially ones that include theory-driven or experimentally-derived predictors, moderators and mediators; and innovations in dissemination and implementation of evidence-based practices into clinical practice in psychology and associated fields, especially those that target underlying mechanisms or focus on novel approaches to treatment delivery. In addition to traditional psychological disorders, the scope of the journal includes behavioural medicine (e.g., chronic pain). The journal will not consider manuscripts dealing primarily with measurement, psychometric analyses, and personality assessment.