Performance of Machine Learning Suicide Risk Models in an American Indian Population
This prognostic study found that existing risk identification models for suicide prevention held promise when applied to new contexts and performed better than relying on a combined indictor of a positive suicide risk screen result, history of attempt, and recent suicidal ideation.
Authors
- Novalene Goklish (ORCID: https://orcid.org/0000-0001-8478-4604)
- Paul Rebman
- Rose Suttle
- Emily E. Haroz (ORCID: https://orcid.org/0000-0003-1833-4925)
- Roy J. Adams (ORCID: https://orcid.org/0000-0001-6859-3007)
- Mitchell Garcia
- Eben Clattenburg
- Joe Mega
- Dominick Maggio
Institutions
- Johns Hopkins University (US)
- Indian Health Service (US)
- Johns Hopkins Medicine (US)
Publication Details
- Journal
- JAMA Network Open
- Published
- 2024-10-14
- DOI
- https://doi.org/10.1001/jamanetworkopen.2024.39269
- Citations
- 14
- Primary Topic
- Suicide and Self-Harm Studies
- Type
- article
- Field-Weighted Citation Impact
- 8.95
Funders
- National Institutes of Health
- National Institute of Mental Health