Abstract

Machine learning (ML) offers robust statistical and probabilistic techniques that can help to make sense of large amounts of data. This scoping review paper aims to broadly explore the nature of research activity using ML in the context of psychological talk therapies, highlighting the scope of current methods and considerations for clinical practice and directions for future research. Using a systematic search methodology, fifty-one studies were identified. A narrative synthesis indicates two types of studies, those who developed and tested an ML model (k=44), and those who reported on the feasibility of a particular treatment tool that uses an ML algorithm (k=7). Most model development studies used supervised learning techniques to classify or predict labeled treatment process or outcome data, whereas others used unsupervised techniques to identify clusters in the unlabeled patient or treatment data. Overall, the current applications of ML in psychotherapy research demonstrated a range of possible benefits for indications of treatment process, adherence, therapist skills and treatment response prediction, as well as ways to accelerate research through automated behavioral or linguistic process coding. Given the novelty and potential of this research field, these proof-of-concept studies are encouraging, however, do not necessarily translate to improved clinical practice (yet).

摘要

机器学习（ML）提供了强大的统计和概率技术，可以帮助理解大量数据。这份范围广泛的综述文章旨在广泛探讨在心理谈话疗法中使用ML进行的研究活动的性质，强调当前方法的范围和临床实践的考虑因素以及未来研究的方向。使用系统的搜索方法，确定了五十一项研究。叙述性综合说明了两种类型的研究，即开发和测试ML模型的研究（k = 44），以及报告了使用ML算法的特定治疗工具的可行性的研究（k= 7）。大多数模型开发研究使用监督学习技术对分类的治疗过程或结果数据进行分类或预测，而其他模型研究则使用无监督的技术来识别未标记的患者或治疗数据中的聚类。总体而言，ML在心理治疗研究中的当前应用证明了对治疗过程，依从性，治疗师技能和治疗反应预测的适应症的一系列可能的益处，以及通过自动行为或语言过程编码来加速研究的方法。考虑到该研究领域的新颖性和潜力，这些概念验证研究令人鼓舞，但是不一定转化为改善的临床实践（尚未）。