Using machine learning algorithms to predict the effects of change processes in psychotherapy: Toward process-level treatment personalization.

IF 2.6 2区 心理学 Q2 PSYCHOLOGY, CLINICAL Psychotherapy Pub Date : 2023-12-01 Epub Date: 2023-10-05 DOI:10.1037/pst0000507
Juan Martín Gómez Penedo, Julian Rubel, Manuel Meglio, Leo Bornhauser, Tobias Krieger, Anna Babl, Roberto Muiños, Andrés Roussos, Jaime Delgadillo, Christoph Flückiger, Thomas Berger, Wolfgang Lutz, Martin Grosse Holtforth
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Abstract

This study aimed to develop and test algorithms to determine the individual relevance of two psychotherapeutic change processes (i.e., mastery and clarification) for outcome prediction. We measured process and outcome variables in a naturalistic outpatient sample treated with an integrative treatment for a variety of diagnoses (n = 608) during the first 10 sessions. We estimated individual within-patient effects of each therapist-evaluated process of change on patient-evaluated subsequent outcomes on a session-by-session basis. Using patients' baseline characteristics, we trained machine learning algorithms on a randomly selected subsample (n = 407) to predict the effects of patients' process variables on outcome. We subsequently tested the predictive capacity of the best algorithm for each process on a holdout subsample (n = 201). We found significant within-patient effects of therapist perceived mastery and clarification on subsequent outcome. In the holdout subsample, the best-performing algorithms resulted in significant but small-to-medium correlations between the predicted and observed relevance of therapist perceived mastery (r = .18) and clarification (r = .16). Using the algorithms to create criteria for individual recommendations, in the holdout sample, we identified patients for whom mastery (14%) or clarification (18%) were indicated. In the mastery-indicated group, a greater focus on mastery was moderately associated with better outcome (r = .33, d = .70), while in the clarification-indicated group, the focus was not related to outcome (r = -.05, d = .10). Results support the feasibility of performing individual predictions regarding mastery process relevance that can be useful for therapist feedback and treatment recommendations. However, results will need to be replicated with prospective experimental designs. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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使用机器学习算法预测心理治疗中变化过程的影响:走向过程级治疗个性化。
本研究旨在开发和测试算法,以确定两个心理治疗变化过程(即掌握和澄清)与结果预测的个体相关性。我们测量了在前10个疗程中接受综合治疗的自然门诊样本(n=608)的过程和结果变量。我们估计了每个治疗师评估的变化过程对患者的个体内影响,并在逐个疗程的基础上评估了后续结果。使用患者的基线特征,我们在随机选择的子样本(n=407)上训练机器学习算法,以预测患者的过程变量对结果的影响。随后,我们在保持子样本(n=201)上测试了每个过程的最佳算法的预测能力。我们发现治疗师感知掌握和澄清对后续结果的患者内影响显著。在拒绝子样本中,表现最好的算法导致治疗师感知掌握的预测和观察相关性(r=.18)与澄清(r=.16)之间存在显著但中小的相关性。在拒绝样本中,使用算法为个人推荐创建标准,我们确定了需要掌握(14%)或澄清(18%)的患者。在掌握指示组中,更关注掌握与更好的结果适度相关(r=.33,d=.70),而在澄清指示组中,重点与结果无关(r=-0.05,d=.10)。结果支持对掌握过程相关性进行个体预测的可行性,这对治疗师的反馈和治疗建议很有用。然而,结果将需要用前瞻性的实验设计来复制。(PsycInfo数据库记录(c)2023 APA,保留所有权利)。
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来源期刊
Psychotherapy
Psychotherapy PSYCHOLOGY, CLINICAL-
CiteScore
4.60
自引率
12.00%
发文量
93
期刊介绍: Psychotherapy Theory, Research, Practice, Training publishes a wide variety of articles relevant to the field of psychotherapy. The journal strives to foster interactions among individuals involved with training, practice theory, and research since all areas are essential to psychotherapy. This journal is an invaluable resource for practicing clinical and counseling psychologists, social workers, and mental health professionals.
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