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Improving Predictive Modeling With Machine Learning Techniques

  • There is no longer any doubt about the general effectiveness of psychotherapy. However, up to 40% of patients do not respond to treatment. Despite efforts to develop new treatments, overall effectiveness has not improved. Consequently, practice-oriented research has emerged to make research results more relevant to practitioners. Within this context, patient-focused research (PFR) focuses on the question of whether a particular treatment works for a specific patient. Finally, PFR gave rise to the precision mental health research movement that is trying to tailor treatments to individual patients by making data-driven and algorithm-based predictions. These predictions are intended to support therapists in their clinical decisions, such as the selection of treatment strategies and adaptation of treatment. The present work summarizes three studies that aim to generate different prediction models for treatment personalization that can be applied to practice. The goal of Study I was to develop a model for dropout prediction using data assessed prior to the first session (N = 2543). The usefulness of various machine learning (ML) algorithms and ensembles was assessed. The best model was an ensemble utilizing random forest and nearest neighbor modeling. It significantly outperformed generalized linear modeling, correctly identifying 63.4% of all cases and uncovering seven key predictors. The findings illustrated the potential of ML to enhance dropout predictions, but also highlighted that not all ML algorithms are equally suitable for this purpose. Study II utilized Study I’s findings to enhance the prediction of dropout rates. Data from the initial two sessions and observer ratings of therapist interventions and skills were employed to develop a model using an elastic net (EN) algorithm. The findings demonstrated that the model was significantly more effective at predicting dropout when using observer ratings with a Cohen’s d of up to .65 and more effective than the model in Study I, despite the smaller sample (N = 259). These results indicated that generating models could be improved by employing various data sources, which provide better foundations for model development. Finally, Study III generated a model to predict therapy outcome after a sudden gain (SG) in order to identify crucial predictors of the upward spiral. EN was used to generate the model using data from 794 cases that experienced a SG. A control group of the same size was also used to quantify and relativize the identified predictors by their general influence on therapy outcomes. The results indicated that there are seven key predictors that have varying effect sizes on therapy outcome, with Cohen's d ranging from 1.08 to 12.48. The findings suggested that a directive approach is more likely to lead to better outcomes after an SG, and that alliance ruptures can be effectively compensated for. However, these effects were reversed in the control group. The results of the three studies are discussed regarding their usefulness to support clinical decision-making and their implications for the implementation of precision mental health.
Author:Björn Bennemann
Advisor:Wolfgang Lutz, Julian Amadeus Rubel
Document Type:Doctoral Thesis
Date of completion:2023/12/21
Publishing institution:Universität Trier
Granting institution:Universität Trier, Fachbereich 1
Date of final exam:2023/04/25
Release Date:2024/01/16
GND Keyword:Klient; Kognitive Verhaltenstherapie; Maschinelles Lernen; Prognose; Psychotherapeut; Psychotherapie; Therapieabbruch; Therapieerfolg
Number of pages:xiv, 160
First page:iii
Last page:160
Institutes:Fachbereich 1
Dewey Decimal Classification:1 Philosophie und Psychologie / 15 Psychologie / 150 Psychologie
Licence (German):License LogoCC BY-SA: Creative-Commons-Lizenz 4.0 International

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