Machine Learning–Based Early Warning Systems for Therapy Failure in High-Conflict Couples

Abstract

The objective of this study was to develop and evaluate a machine learning–based early warning system capable of predicting therapy failure trajectories among high-conflict couples during the early phases of couple therapy. This longitudinal observational study was conducted with high-conflict couples undergoing outpatient couple therapy in Germany. Multimodal data were collected from both partners and therapists across early and mid-treatment sessions, including self-reported relational functioning, emotional regulation indicators, therapeutic alliance ratings, therapist session evaluations, ecological momentary assessments, and automated interactional features derived from session recordings. Therapy failure was operationalized as premature dropout, therapist-rated non-response, or reliable deterioration in relationship satisfaction over time. Multiple machine learning models, including regularized logistic regression, random forest, gradient boosting, and recurrent neural networks, were trained using longitudinal features capturing both static baseline characteristics and dynamic process indicators. Model performance was evaluated using cross-validated inferential metrics emphasizing early detection accuracy. Inferential analyses demonstrated that all machine learning models significantly outperformed chance-level prediction, with recurrent neural network models yielding the highest discriminative accuracy and sensitivity for early therapy failure detection. Dynamic process variables, particularly early-session therapeutic alliance variability, escalating conflict trajectories, emotional spillover volatility, and dyadic interaction asymmetries, showed statistically stronger predictive contributions than baseline relational characteristics. The early warning system successfully identified a substantial proportion of therapy failure cases within the first four therapy sessions, indicating robust temporal predictive validity. The findings indicate that therapy failure in high-conflict couples follows identifiable dynamic patterns that can be detected early using machine learning approaches. Implementing early warning systems in couple therapy may enable proactive, adaptive interventions that reduce dropout and non-response, thereby improving therapeutic outcomes for high-conflict couples.