Artificial intelligence generally and machine learning specifically have become deeply woven into the lives and technologies of modern life. Machine learning is dramatically changing scientific research and industry and may also hold promise for addressing limitations encountered in mental health care and psychotherapy. The current paper introduces machine learning and natural language processing as related methodologies that may prove valuable for automating the assessment of meaningful aspects of treatment. Prediction of therapeutic alliance from session recordings is used as a case in point. Recordings from 1,235 sessions of 386 clients seen by 40 therapists at a university counseling center were processed using automatic speech recognition software. Machine learning algorithms learned associations between client ratings of therapeutic alliance exclusively from session linguistic content. Using a portion of the data to train the model, machine learning algorithms modestly predicted alliance ratings from session content in an independent test set (Spearman's ρ = .15, p < .001). These results highlight the potential to harness natural language processing and machine learning to predict a key psychotherapy process variable that is relatively distal from linguistic content. Six practical suggestions for conducting psychotherapy research using machine learning are presented along with several directions for future research. Questions of dissemination and implementation may be particularly important to explore as machine learning improves in its ability to automate assessment of psychotherapy process and outcome. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

中文翻译：

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心理治疗研究中的机器学习和自然语言处理：联盟作为示例用例。


人工智能，特别是机器学习，已经深深融入现代生活的生活和技术中。机器学习正在极大地改变科学研究和行业，也可能有望解决精神卫生保健和心理治疗中遇到的局限性。当前的论文介绍了机器学习和自然语言处理作为相关方法，这些方法可能对于自动评估治疗的有意义方面有价值。从会话记录中预测治疗联盟就是一个恰当的例子。使用自动语音识别软件处理了大学咨询中心 40 名治疗师对 386 名客户进行的 1,235 次咨询的录音。机器学习算法仅从会话语言内容中学习治疗联盟的客户评分之间的关​​联。使用部分数据来训练模型，机器学习算法根据独立测试集中的会话内容适度预测联盟评级（Spearman 的 ρ = .15，p < .001）。这些结果凸显了利用自然语言处理和机器学习来预测与语言内容相对遥远的关键心理治疗过程变量的潜力。提出了利用机器学习进行心理治疗研究的六项实用建议以及未来研究的几个方向。随着机器学习自动评估心理治疗过程和结果的能力的提高，传播和实施的问题可能尤为重要。 （PsycInfo 数据库记录 (c) 2020 APA，保留所有权利）。