Machine learning for prediction of childhood mental health problems in social care
Machine learning performance on this prediction task was promising. Predictive performance in social care settings can be bolstered by linking diverse routinely collected data-sets, making available a range of heterogenous risk factors relating to clinical, social and environmental exposures.
Authors
- Katherine Parkin (ORCID: https://orcid.org/0000-0001-7338-5667)
- Anna Moore (ORCID: https://orcid.org/0000-0001-9614-3812)
- Yasmin Friedmann (ORCID: https://orcid.org/0000-0002-7416-9867)
- Ryan Crowley (ORCID: https://orcid.org/0000-0002-1482-5631)
- Efthalia Massou (ORCID: https://orcid.org/0000-0003-0488-482X)
- Rachel Sippy (ORCID: https://orcid.org/0000-0003-3617-2093)
- Píetro Lió (ORCID: https://orcid.org/0000-0002-0540-5053)
- Ann Mary John (ORCID: https://orcid.org/0000-0002-5657-6995)
- Emma Rocheteau (ORCID: https://orcid.org/0000-0002-6450-0878)
Institutions
- University of Cambridge (GB)
- Anna Freud Centre (GB)
- Neath Port Talbot College (GB)
- Neath Port Talbot Hospital (GB)
- Swansea University (GB)
- Cambridgeshire and Peterborough NHS Foundation Trust (GB)
- New York University (US)
Publication Details
- Journal
- BJPsych Open
- Published
- 2025-04-11
- DOI
- https://doi.org/10.1192/bjo.2025.32
- Citations
- 3
- Primary Topic
- Emergency and Acute Care Studies
- Type
- article
- Field-Weighted Citation Impact
- 6.76
Funders
- MQ: Transforming Mental Health
- National Institute for Health and Care Research
- Medical Research Council
- School for Public Health Research
- NIHR Cambridge Biomedical Research Centre