A multilevel machine learning algorithm to predict session-by-session outcome for patients receiving cognitive-behavioural therapy
This study provides preliminary support for the integration of multilevel and machine learning models via generalized linear mixed model trees. The algorithms developed could help support routine implementation of precision mental health care strategies by informing therapists' treatment planning and session-by-session responsiveness for different patient subgroups.
Authors
- Julian Rubel (ORCID: https://orcid.org/0000-0002-9625-6611)
- Juan Martín Gómez Penedo (ORCID: https://orcid.org/0000-0001-7304-407X)
- Alice E. Coyne (ORCID: https://orcid.org/0000-0002-5950-0486)
- Wolfgang Lutz (ORCID: https://orcid.org/0000-0002-5141-3847)
- Marjolein Fokkema (ORCID: https://orcid.org/0000-0002-9252-8325)
- Manuel Meglio (ORCID: https://orcid.org/0000-0001-6955-7585)
- Rebekka Wassmann
Institutions
- Leiden University (NL)
- Trier University of Applied Sciences (DE)
- Consejo Nacional de Investigaciones Científicas y Técnicas (AR)
- Osnabrück University (DE)
- University of Applied Sciences Leiden (NL)
- Asbury University (US)
- Universidad de Buenos Aires (AR)
- Universität Trier (DE)
Publication Details
- Journal
- Behaviour Research and Therapy
- Published
- 2025-09-04
- DOI
- https://doi.org/10.1016/j.brat.2025.104848
- Primary Topic
- Mental Health Research Topics
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Deutsche Forschungsgemeinschaft