AI-based prediction of depression symptomatology in first-episode psychosis patients: insights from the EUFEST and RAISE-ETP clinical trials
Predictive models using clinical data, quality of life, and biomarkers accurately forecast depressive events in FEP, demonstrating generalization across populations.
Authors
- Oliver Howes (ORCID: https://orcid.org/0000-0002-2928-1972)
- John M. Kane (ORCID: https://orcid.org/0000-0002-2628-9442)
- Paris Alexandros Lalousis (ORCID: https://orcid.org/0000-0001-9642-3080)
- Clara Vetter (ORCID: https://orcid.org/0000-0003-4268-2890)
- Rachel Upthegrove (ORCID: https://orcid.org/0000-0001-8204-5103)
- Fiona Coutts (ORCID: https://orcid.org/0009-0009-8951-1467)
- Jana von Trott
- Sergio Mena (ORCID: https://orcid.org/0000-0002-5102-2336)
- Nikolaos Koutsouleris (ORCID: https://orcid.org/0000-0001-6825-6262)
- W. Wolfgang Fleischhacker
- René R. Kahn
- Esin Ucur
Institutions
- Northwell Health (US)
- Innsbruck Medical University (AT)
- King's College London (GB)
- University Medical Center Utrecht (NL)
- University of Oxford (GB)
- Max Planck Institute of Psychiatry (DE)
- Zucker Hillside Hospital (US)
- University of Birmingham (GB)
- Ludwig-Maximilians-Universität München (DE)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- Psychological Medicine
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1017/s0033291725100950
- Primary Topic
- Schizophrenia research and treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Pfizer
- AstraZeneca
- Sanofi
- National Institute of Mental Health