Prospective prediction of first onset of major depressive disorder in midlife using machine learning
Although the prediction models achieved discriminative performance levels above chance, further refinement is necessary. The addition of biological and genetic predictors did not elevate performance markedly. Additional research seems warranted given the limited number and heterogeneous nature of existing studies, the burden associated with MDD, and the potential to improve overall outcomes for people at risk for MDD.
Authors
- Marie‐Pierre F. Strippoli (ORCID: https://orcid.org/0000-0003-3053-484X)
- Martin Preisig (ORCID: https://orcid.org/0000-0001-5689-4259)
- Giorgio Pistis (ORCID: https://orcid.org/0000-0002-4525-4608)
- Johannes Massell (ORCID: https://orcid.org/0000-0002-6565-2590)
- Marcel Miché (ORCID: https://orcid.org/0000-0001-8838-1749)
- Roselind Lieb (ORCID: https://orcid.org/0000-0002-2039-2262)
Institutions
- University of Basel (CH)
- Hôpital de Cery (CH)
- Department of Biomedicine Basel (CH)
- University of Lausanne (CH)
Publication Details
- Journal
- Social Psychiatry and Psychiatric Epidemiology
- Published
- 2025-06-18
- DOI
- https://doi.org/10.1007/s00127-025-02942-z
- Primary Topic
- Mental Health Research Topics
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Universität Basel