Predicting criminal offence in adolescents who exhibit antisocial behaviour: a machine learning study using data from a large randomised controlled trial of multisystemic therapy
The potential utility of ML approaches for accurately predicting criminal offences in high-risk youth is demonstrated. Interpretable ML-based predictive models could be utilised in youth services or research to help develop and deliver effective interventions.
Authors
- Stephen Butler (ORCID: https://orcid.org/0000-0002-2072-5338)
- James M. S. Wason (ORCID: https://orcid.org/0000-0002-4691-126X)
- Abdullah Kraam (ORCID: https://orcid.org/0000-0003-3909-481X)
- Peter Fonagy (ORCID: https://orcid.org/0000-0003-0229-0091)
- Stephen Scott (ORCID: https://orcid.org/0000-0003-4680-6213)
- Stephen Pilling (ORCID: https://orcid.org/0000-0002-7361-8202)
- Jae Won Suh (ORCID: https://orcid.org/0009-0004-0962-3850)
- David Cottrell (ORCID: https://orcid.org/0000-0001-8674-0955)
- Elizabeth Simes (ORCID: https://orcid.org/0000-0003-1704-6278)
- Rob Saunders (ORCID: https://orcid.org/0000-0002-7077-8729)
- Henry Delamain (ORCID: https://orcid.org/0000-0002-1757-652X)
- Ian M. Goodyer
Institutions
- University of Leeds (GB)
- University of Prince Edward Island (CA)
- University of Cambridge (GB)
- Health Data Research UK (GB)
- King's College Hospital (GB)
- University College London (GB)
- Newcastle University (GB)
Publication Details
- Journal
- European Child & Adolescent Psychiatry
- Published
- 2024-10-08
- DOI
- https://doi.org/10.1007/s00787-024-02592-7
- Citations
- 2
- Primary Topic
- Crime Patterns and Interventions
- Type
- article
- Field-Weighted Citation Impact
- 2.26
Funders
- National Institute for Health and Care Research
- Department of Health and Social Care