Identifying high-dose opioid prescription risks using machine learning: A focus on sociodemographic characteristics
This analysis underscores the significant influence of sociodemographic variables on high-dose opioid prescriptions. The interplay of these factors highlights the need for multifaceted public health strategies to address the underlying complexities of the opioid crisis. The integration of machine learning methods with traditional epidemiological techniques represents a promising approach for gaining a comprehensive understanding of intricate patterns not captured in traditional statistical analysis, thereby enabling effective mitigation of the opioid crisis.
Authors
- Praveen Rao
- Butros M. Dahu (ORCID: https://orcid.org/0000-0001-5880-2450)
- Timothy L. Haithcoat (ORCID: https://orcid.org/0000-0002-7353-4834)
- Tracy Greever-Rice
- Xing Song (ORCID: https://orcid.org/0000-0002-3712-2904)
- Mirna Becevic (ORCID: https://orcid.org/0000-0001-6520-1581)
- M.A. Mohamed Sahul Hameed
- Douglas Burgess
- Olubukola I Ogundele
Institutions
- University of Missouri Health System (US)
- St. Mary's Health Center (US)
- Institute for Medical Informatics and Biostatistics (CH)
- University of Missouri–Kansas City (US)
Publication Details
- Journal
- Journal of Opioid Management
- Published
- 2025-04-18
- DOI
- https://doi.org/10.5055/jom.0924
- Primary Topic
- Opioid Use Disorder Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00