Our expertise comprises the application of machine learning methods in the field of psychotherapy (research). The analysis of patient data is intended to support clinical decisions in prognostics, differential indication and evaluation, which should increase the effectiveness of psychotherapies (maximisation of positive and minimisation of negative effects).
Involved Research Groups
- Evaluation of large psychotherapeutic datasets regarding predictors of outcome and identification of mechanisms of change.
- Routine Outcome Monitoring in psychotherapy
- Ecological Momentary Assessment for the prediction of problem behaviour
- Kaiser, T., & Laireiter, A. R. (2017). DynAMo: A Modular Platform for Monitoring Process, Outcome, and Algorithm-Based Treatment Planning in Psychotherapy. JMIR Medical Informatics, 5(3), e20. https://doi.org/10.2196/medinform.6808
- Herzog, P., Feldmann, M., Voderholzer, U., Gärtner, T., Armbrust, M., Rauh, E., Doerr, R., Rief, W., & Brakemeier, E.-L. (in revision). Drawing the Borderline: Predicting treatment outcomes in Patients with borderline personality disorder, Behavior Research and Therapy
- Herzog, P., Voderholzer, U., Gärtner, T., Osen, B., Svitak, M., Doerr, R., Rolvering-Dijkstra, M., Feldmann, M., Rief, W., & Brakemeier, E.-L. (in revision). Identifying predictors of treatment outcome in posttraumatic stress disorder: A naturalistic, multi-site, single-treatment study based on machine learning, Psychotherapy Research