Kuo Patty B, Tanana Michael J, Goldberg Simon B, Caperton Derek D, Narayanan Shrikanth, Atkins David C, Imel Zac E
University of Utah.
University of Wisconsin, Madison.
Clin Psychol Sci. 2024 May;12(3):435-446. doi: 10.1177/21677026231172694. Epub 2023 Jun 1.
Natural language processing (NLP) is a subfield of machine learning that may facilitate the evaluation of therapist-client interactions and provide feedback to therapists on client outcomes on a large scale. However, there have been limited studies applying NLP models to client outcome prediction that have (a) used transcripts of therapist-client interactions as direct predictors of client symptom improvement, (b) accounted for contextual linguistic complexities, and (c) used best practices in classical training and test splits in model development. Using 2,630 session recordings from 795 clients and 56 therapists, we developed NLP models that directly predicted client symptoms of a given session based on session recordings of the previous session (Spearman's rho =0.32, p<.001). Our results highlight the potential for NLP models to be implemented in outcome monitoring systems to improve quality of care. We discuss implications for future research and applications.
自然语言处理（NLP）是机器学习的一个子领域，它可能有助于评估治疗师与来访者的互动，并大规模地向治疗师提供关于来访者治疗结果的反馈。然而，将NLP模型应用于来访者治疗结果预测的研究有限，这些研究（a）将治疗师与来访者互动的记录文本用作来访者症状改善的直接预测指标，（b）考虑了上下文语言复杂性，以及（c）在模型开发中采用了经典训练和测试分割的最佳实践。我们使用来自795名来访者和56名治疗师的2630次会话记录，开发了NLP模型，该模型基于上一次会话的记录直接预测给定会话中来访者的症状（斯皮尔曼等级相关系数rho = 0.32，p <.001）。我们的结果突出了NLP模型在结果监测系统中实施以提高护理质量的潜力。我们讨论了对未来研究和应用的影响。
