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For whom should psychotherapy focus on problem coping? A machine learning algorithm for treatment personalization.
Gómez Penedo, Juan Martin; Schwartz, Brian; Giesemann, Julia; Rubel, Julian A; Deisenhofer, Anne-Katharina; Lutz, Wolfgang.
Afiliação
  • Gómez Penedo JM; Facultad de Psicología, Universidad de Buenos Aires (Conicet), Buenos Aires, Argentina.
  • Schwartz B; Department of Psychology, University of Trier, Trier, Germany.
  • Giesemann J; Department of Psychology, University of Trier, Trier, Germany.
  • Rubel JA; Department of Psychology, University of Trier, Trier, Germany.
  • Deisenhofer AK; Department of Psychology, Justus-Liebig University Giessen, Giessen, Germany.
  • Lutz W; Department of Psychology, University of Trier, Trier, Germany.
Psychother Res ; 32(2): 151-164, 2022 02.
Article em En | MEDLINE | ID: mdl-34034627
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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Terapia Cognitivo-Comportamental / Aprendizado de Máquina Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Terapia Cognitivo-Comportamental / Aprendizado de Máquina Idioma: En Ano de publicação: 2022 Tipo de documento: Article