For whom should psychotherapy focus on problem coping? A machine learning algorithm for treatment personalization

• Published in: Psychotherapy research Vol. 32; no. 2; pp. 151 - 164
• Main Authors: Gómez Penedo, Juan Martin, Schwartz, Brian, Giesemann, Julia, Rubel, Julian A., Deisenhofer, Anne-Katharina, Lutz, Wolfgang
• Format: Journal Article
• Language: English
• Published: England Routledge 17.02.2022; Taylor & Francis Ltd
• Subjects: Adaptation, Psychological; Algorithms; baseline patient characteristics; Clinical medicine; Cognitive Behavioral Therapy; Cognitive-behavioral factors; cognitive-behavioral therapy (CBT); Coping; Humans; individual predictions; Machine Learning; Outpatient treatment; Problem coping experiences; Psychotherapy; Suitability; baseline patient characteristics; machine learning; Problem coping experiences; individual predictions; cognitive-behavioral therapy (CBT)
• ISSN: 1050-3307; 1468-4381
• DOI: 10.1080/10503307.2021.1930242

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.