Using machine learning methods to identify trajectories of change and predict responders and non-responders to short-term dynamic therapy.

IF 2.6 1区 心理学 Q2 PSYCHOLOGY, CLINICAL
Refael Yonatan-Leus, Gershom Gwertzman, Orya Tishby
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引用次数: 0

Abstract

Objectives: Predicting therapy responders can significantly improve clinical outcomes. This study aims to identify predictors of response to short-term dynamic therapy.

Methods: Data from 95 patients who underwent 16-session therapy were analyzed using machine learning. Weekly progress was monitored with the Outcome Questionnaire (OQ45) and Target Complaints (TC). A machine learning model identified change trajectories for responders and non-responders, with a random forest algorithm and elastic net modeling predicting trajectory group membership using pre-treatment data.

Results: A weak positive relationship was found between the trajectories of the two outcome variables. The results of the different analysis methods were compared and discussed. Important predictors of OQ45 trajectories, based on random forest modeling, included initial symptom severity, difficulties in emotion regulation, coldness, avoidant attachment, conscientiousness, interpersonal problems, non-acceptance of negative emotion, neuroticism, emotional clarity, impulsivity, and emotion awareness (72.8% accuracy). Initial problem severity, self-scarifying extraversion, and non-assertiveness were the most dominant predictors for TC trajectories (62.8% accuracy).

Conclusions: These findings offer data-driven insights for selecting short-term dynamic therapy. Predicting response for the OQ45, a nomothetic measure, does not extend to the TC, an idiographic measure, and vice versa, highlighting the importance of multidimensional outcome evaluations for personalized treatment.

使用机器学习方法识别变化轨迹,预测短期动态疗法的应答者和非应答者。
目的:预测治疗反应者可显著改善临床疗效。本研究旨在确定短期动态疗法反应的预测因素:方法:使用机器学习对 95 名接受了 16 个疗程治疗的患者的数据进行分析。通过结果问卷(OQ45)和目标投诉(TC)监测每周的进展情况。机器学习模型确定了应答者和非应答者的变化轨迹,通过随机森林算法和弹性网建模,利用治疗前数据预测轨迹组的成员:结果:两个结果变量的变化轨迹之间存在微弱的正相关关系。对不同分析方法的结果进行了比较和讨论。根据随机森林建模,OQ45轨迹的重要预测因子包括初始症状严重程度、情绪调节困难、冷漠、回避型依恋、自觉性、人际关系问题、不接受负面情绪、神经质、情绪清晰度、冲动性和情绪意识(准确率为72.8%)。最初的问题严重性、自我批评型外向性和非自负性是预测自闭症轨迹的最主要因素(准确率为 62.8%):这些发现为选择短期动态疗法提供了数据驱动的见解。这些研究结果为选择短期动态疗法提供了数据驱动的见解。对OQ45(一种提名测量)的反应预测并不能延伸到TC(一种特异测量),反之亦然,这凸显了多维结果评估对个性化治疗的重要性。
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来源期刊
Psychotherapy Research
Psychotherapy Research PSYCHOLOGY, CLINICAL-
CiteScore
7.80
自引率
10.30%
发文量
68
期刊介绍: Psychotherapy Research seeks to enhance the development, scientific quality, and social relevance of psychotherapy research and to foster the use of research findings in practice, education, and policy formulation. The Journal publishes reports of original research on all aspects of psychotherapy, including its outcomes, its processes, education of practitioners, and delivery of services. It also publishes methodological, theoretical, and review articles of direct relevance to psychotherapy research. The Journal is addressed to an international, interdisciplinary audience and welcomes submissions dealing with diverse theoretical orientations, treatment modalities.
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