Identifying key risk factors for intentional self‐harm, including suicide, among a cohort of people prescribed opioid agonist treatment: A predictive modelling study
Among people in opioid agonist treatment, administrative linked data can be used with advanced machine learning algorithms to predict self-harm and/or suicide in a 30-day prediction window.
Authors
- Matthew Hickman (ORCID: https://orcid.org/0000-0001-9864-459X)
- Suzanne Nielsen (ORCID: https://orcid.org/0000-0001-5341-1055)
- Louisa Degenhardt (ORCID: https://orcid.org/0000-0002-8513-2218)
- Chrianna Bharat (ORCID: https://orcid.org/0000-0002-9778-0386)
- N. R. Jones (ORCID: https://orcid.org/0000-0002-5742-4598)
- Julia Lappin (ORCID: https://orcid.org/0000-0001-5946-2144)
- Sarah Larney (ORCID: https://orcid.org/0000-0002-5602-4963)
- Nimnaz Fathima Ghouse
Institutions
- The University of Sydney (AU)
- UNSW Sydney (AU)
- University of Bristol (GB)
- Eastern Health (AU)
- Centre Hospitalier de l’Université de Montréal (CA)
- Monash University (AU)
- Université de Montréal (CA)
Publication Details
- Journal
- Addiction
- Published
- 2025-05-25
- DOI
- https://doi.org/10.1111/add.70095
- Citations
- 1
- Primary Topic
- Suicide and Self-Harm Studies
- Type
- article
- Field-Weighted Citation Impact
- 2.31
Funders
- Health Research
- NIHR Bristol Biomedical Research Centre
- Australian Government
- National Institute for Health and Care Research
- University of Bristol
- Department of Health and Aged Care, Australian Government
- NSW Ministry of Health
- NSW Health
- National Coronial Information System
- National Institutes of Health
- Medical Research Council
- National Health and Medical Research Council
- National Drug and Alcohol Research Centre
- Fogarty International Center