Predicting criminal offence in adolescents who exhibit antisocial behaviour: A machine learning study using data from a large randomised controlled trial of Multisystemic therapy

Suh, J.W., Saunders, R., Simes, E. et al. (9 more authors) (2024) Predicting criminal offence in adolescents who exhibit antisocial behaviour: A machine learning study using data from a large randomised controlled trial of Multisystemic therapy. European Child and Adolescent Psychiatry. ISSN 1018-8827

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Item Type: Article
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© The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Keywords: Criminal offending, Recidivism, Youth crime, Machine learning, Prediction modelling
Dates:
  • Published (online): 8 October 2024
  • Accepted: 30 September 2024
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Health Sciences (Leeds) > Academic Unit of Psychiatry and Behavioural Sciences (Leeds)
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NIHR National Inst Health Research
CRJK
Depositing User: Symplectic Publications
Date Deposited: 04 Oct 2024 08:55
Last Modified: 17 Oct 2024 15:44
Published Version: https://link.springer.com/article/10.1007/s00787-0...
Status: Published online
Publisher: Springer
Identification Number: 10.1007/s00787-024-02592-7
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