Predicting incident cardio-metabolic disease among persons with and without depressive and anxiety disorders: a machine learning approach
In this longitudinal study, ML classifiers were not able to accurately predict 9-year CMD onset in a sample enriched of subjects with psychopathology. Detailed psychiatric/biological information did not substantially increase predictive performance.
Authors
- Wessel A. van Eeden (ORCID: https://orcid.org/0000-0002-8913-0615)
- George Aalbers (ORCID: https://orcid.org/0000-0002-7140-1536)
- Arja O. Rydin (ORCID: https://orcid.org/0000-0002-5525-3350)
- Yuri Milaneschi (ORCID: https://orcid.org/0000-0002-3697-6617)
- Brenda W.J.H. Penninx (ORCID: https://orcid.org/0000-0001-7779-9672)
- Femke Lamers (ORCID: https://orcid.org/0000-0003-4344-5766)
Institutions
- Leiden University (NL)
- Amsterdam Neuroscience (NL)
- GGD Amsterdam (NL)
- Public Health Service of Amsterdam (NL)
- Amsterdam University Medical Centers (NL)
- Vrije Universiteit Amsterdam (NL)
Publication Details
- Journal
- Social Psychiatry and Psychiatric Epidemiology
- Published
- 2025-02-18
- DOI
- https://doi.org/10.1007/s00127-025-02857-9
- Primary Topic
- Cardiac Health and Mental Health
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Medical Research Council