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Although risk factors are well known, most tools for identifying individuals at highest risk of these behaviours have limited clinical value. We aimed to develop and internally validate models to predict intentional self\u2010harm and suicide risk among people who have been in opioid agonist treatment (OAT).<\/jats:p><\/jats:sec><jats:sec><jats:title>Design<\/jats:title><jats:p>Retrospective observational cohort study using linked administrative data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Setting<\/jats:title><jats:p>New South Wales, Australia.<\/jats:p><\/jats:sec><jats:sec><jats:title>Participants<\/jats:title><jats:p>46\u2009330 people prescribed OAT between January 2005 and November 2017.<\/jats:p><\/jats:sec><jats:sec><jats:title>Measurements<\/jats:title><jats:p>Intentional self\u2010harm and suicide prediction within a 30\u2010day window using linked population datasets for OAT, hospitalisation, mental health care, incarceration and mortality. Machine learning algorithms, including neural networks and gradient boosting, assessed over 80 factors during the last 3, 6 and 12\u2009months. Feature visualisation using SHapley Additive exPlanations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Findings<\/jats:title><jats:p>Gradient boosting identified 30 important factors in predicting self\u2010harm and\/or suicide. These included the most recent frequency of emergency department presentations; hospital admissions involving mental disorders such as borderline personality, substance dependence, psychosis and depression\/anxiety; and recent release from incarceration. The best fitting model had a Gini coefficient of 0.65 [area under the curve (AUC)\u2009=\u20090.82] and was applied to 2017 data to estimate the probability of self\u2010harm and\/or suicide. On average 46 people (0.16%) (from a total of 28\u2009000 people in OAT) experienced intentional self\u2010harm or suicide per month. Applying a 0.15% probability threshold, approximately 5167 people were classified as high risk, identifying 69% of all self\u2010harm or suicide cases per month. This figure reduced to 450 per month after excluding people already identified in the previous month.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Among people in opioid agonist treatment, administrative linked data can be used with advanced machine learning algorithms to predict self\u2010harm and\/or suicide in a 30\u2010day prediction window.<\/jats:p><\/jats:sec>","DOI":"10.1111\/add.70095","type":"journal-article","created":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T02:29:56Z","timestamp":1748226596000},"page":"2044-2054","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Identifying key risk factors for intentional self\u2010harm, including suicide, among a cohort of people prescribed opioid agonist treatment: A predictive modelling study"],"prefix":"10.1111","volume":"120","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5742-4598","authenticated-orcid":false,"given":"Nicola R.","family":"Jones","sequence":"first","affiliation":[{"name":"National Drug and Alcohol Research Centre, University of New South Wales  Sydney Australia"},{"name":"SydneyMSK Research Flagship Centre, University of Sydney  Sydney Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9864-459X","authenticated-orcid":false,"given":"Matthew","family":"Hickman","sequence":"additional","affiliation":[{"name":"National Drug and Alcohol Research Centre, University of New South Wales  Sydney Australia"},{"name":"Population Health Sciences Bristol Medical School, University of Bristol  Bristol UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9778-0386","authenticated-orcid":false,"given":"Chrianna","family":"Bharat","sequence":"additional","affiliation":[{"name":"National Drug and Alcohol Research Centre, University of New South Wales  Sydney Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5341-1055","authenticated-orcid":false,"given":"Suzanne","family":"Nielsen","sequence":"additional","affiliation":[{"name":"Monash Addiction Research Centre, Eastern Health Clinical School, Monash University  Melbourne Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5602-4963","authenticated-orcid":false,"given":"Sarah","family":"Larney","sequence":"additional","affiliation":[{"name":"National Drug and Alcohol Research Centre, University of New South Wales  Sydney Australia"},{"name":"Department of Family Medicine and Emergency Medicine Universit\u00e9 de Montr\u00e9al  Montr\u00e9al Canada"},{"name":"Centre de Recherche du Centre Hospitalier de l'Universit\u00e9 de Montr\u00e9al  Montr\u00e9al Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nimnaz Fathima","family":"Ghouse","sequence":"additional","affiliation":[{"name":"National Drug and Alcohol Research Centre, University of New South Wales  Sydney Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5946-2144","authenticated-orcid":false,"given":"Julia","family":"Lappin","sequence":"additional","affiliation":[{"name":"National Drug and Alcohol Research Centre, University of New South Wales  Sydney Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8513-2218","authenticated-orcid":false,"given":"Louisa","family":"Degenhardt","sequence":"additional","affiliation":[{"name":"National Drug and Alcohol Research Centre, University of New South Wales  Sydney Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,5,25]]},"reference":[{"key":"e_1_2_10_2_1","unstructured":"Suicide worldwide in 2019: Global health estimates.Geneva:World Health Organization;2021. 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