Using machine learning methods to identify trajectories of change and predict responders and non-responders to short-term dynamic therapy.
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...
| Publicado en: | Psychotherapy Research Vol. 35; no. 7; pp. 1070 - 1087 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Taylor & Francis Ltd
Sep2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=187564798&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 187564798 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10503307 10T jtl: Psychotherapy Research issn: 10503307 maglogo: N pubinfo: dt: Sep2025 vid: 35 iid: 7 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 187564798 10.1080/10503307.2024.2420725 ppf: 1070 ppct: 17 formats: tig: atl: Using machine learning methods to identify trajectories of change and predict responders and non-responders to short-term dynamic therapy. aug: au: Yonatan-Leus, Refael Gwertzman, Gershom Tishby, Orya affil: Department of Psychology, The College of Management Academic Studies, Rishon LeTsiyon, Israel Department of Psychology, The Hebrew University of Jerusalem, Israel Paul Baerwald School of Social Work and Social Welfare, The Hebrew University of Jerusalem, Israel su: Machine learning Random forest algorithms Psychodynamic psychotherapy Health outcome assessment Treatment effectiveness Emotion regulation Individualized medicine sug: subj: Machine learning Random forest algorithms Psychodynamic psychotherapy Health outcome assessment Treatment effectiveness Emotion regulation Individualized medicine keyword: elastic net modeling machine learning non-responding random-forest short-term psychodynamic therapy suitability to treatment elastic net modeling machine learning non-responding random-forest short-term psychodynamic therapy suitability to treatment ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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