Development of a risk factor framework to inform machine learning prediction of young people’s mental health problems: a Delphi study
This theoretical framework provides a roadmap of important risk factors for inclusion in early identification models to enhance the predictive accuracy of childhood mental health problems. It offers a useful theoretical reference point to support model building for those without domain expertise.
Authors
- Guy C. M. Skinner (ORCID: https://orcid.org/0000-0002-2134-3913)
- Peter Fonagy (ORCID: https://orcid.org/0000-0003-0229-0091)
- Katherine Parkin (ORCID: https://orcid.org/0000-0001-7338-5667)
- Anna Moore (ORCID: https://orcid.org/0000-0001-9614-3812)
- Ryan Crowley (ORCID: https://orcid.org/0000-0002-1482-5631)
- Rachel Sippy (ORCID: https://orcid.org/0000-0003-3617-2093)
- Emily Brewis
- Nicole Marshall (ORCID: https://orcid.org/0000-0001-5368-3368)
- Tara Ramsay-Patel
- Yi Zhang
- Shabina Hayat
- Vahgisha Thirugnanasampanthan
- Carol Brayne
Institutions
- University of Cambridge (GB)
- Cambridge School (PT)
- Bridge University (SS)
- University College London (GB)
- New York University (US)
Publication Details
- Journal
- JAMIA Open
- Published
- 2025-11-03
- DOI
- https://doi.org/10.1093/jamiaopen/ooaf166
- Primary Topic
- Digital Mental Health Interventions
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- UK Research and Innovation
- National Institute for Health and Care Research
- Department of Health and Social Care
- Medical Research Council
- School for Public Health Research
- NIHR Cambridge Biomedical Research Centre