For whom should psychotherapy focus on problem coping? A machine learning algorithm for treatment personalization.
Objective: We aimed to develop and test an algorithm for individual patient predictions of problem coping experiences (PCE) (i.e., patients' understanding and ability to deal with their problems) effects in cognitive–behavioral therapy. Method: In an outpatient sample with a variety of diagnoses (n=...
| Publicado en: | Psychotherapy Research Vol. 32; no. 2; pp. 151 - 165 |
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| Autores principales: | , , , , , |
| Formato: | Artículo |
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Taylor & Francis Ltd
Feb 2022
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| Materias: | |
| 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=154955197&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 154955197 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: Feb 2022 vid: 32 iid: 2 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 154955197 10.1080/10503307.2021.1930242 ppf: 151 ppct: 14 formats: tig: atl: For whom should psychotherapy focus on problem coping? A machine learning algorithm for treatment personalization. aug: au: Gómez Penedo, Juan Martin Schwartz, Brian Giesemann, Julia Rubel, Julian A. Deisenhofer, Anne-Katharina Lutz, Wolfgang affil: Facultad de Psicología, Universidad de Buenos Aires (Conicet), Buenos Aires, Argentina Department of Psychology, University of Trier, Trier, Germany Department of Psychology, Justus-Liebig University Giessen, Giessen, Germany su: Psychotherapy Cognitive therapy Machine learning Random forest algorithms Structural equation modeling sug: subj: Psychotherapy Cognitive therapy Machine learning Random forest algorithms Structural equation modeling keyword: baseline patient characteristics cognitive-behavioral therapy (CBT) individual predictions machine learning Problem coping experiences baseline patient characteristics cognitive-behavioral therapy (CBT) individual predictions machine learning Problem coping experiences ab: Objective: We aimed to develop and test an algorithm for individual patient predictions of problem coping experiences (PCE) (i.e., patients' understanding and ability to deal with their problems) effects in cognitive–behavioral therapy. Method: In an outpatient sample with a variety of diagnoses (n=1010), we conducted Dynamic Structural Equation Modelling to estimate within-patient cross-lagged PCE effects on outcome during the first ten sessions. In a randomly selected training sample (2/3 of the cases), we tried different machine learning algorithms (i.e., ridge regression, LASSO, elastic net, and random forest) to predict PCE effects (i.e., the degree to which PCE was a time-lagged predictor of symptoms), using baseline demographic, diagnostic, and clinically-relevant patient features. Then, we validated the best algorithm on a test sample (1/3 of the cases). Results: The random forest algorithm performed best, explaining 14.7% of PCE effects variance in the training set. The results remained stable in the test set, explaining 15.4% of PCE effects variance. Conclusions: The results show the suitability to perform individual predictions of process effects, based on patients' initial information. If the results are replicated, the algorithm might have the potential to be implemented in clinical practice by integrating it into monitoring and therapist feedback systems. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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