Development and evaluation of a machine learning model to predict acute care for opioid use disorder among Medicaid enrollees engaged in a community‐based treatment program
New machine-learning algorithms perform well to predict risks of opioid use disorder (OUD) acute care use among United States Medicaid enrollees and improve fairness of prediction across racial and ethnic groups compared with previous OUD-related models.
Authors
- Evan S. Cole (ORCID: https://orcid.org/0000-0002-7873-2952)
- Walid F. Gellad (ORCID: https://orcid.org/0000-0002-6992-5197)
- Lu Tang (ORCID: https://orcid.org/0000-0001-6143-9314)
- RuoFei Yin (ORCID: https://orcid.org/0009-0005-4117-380X)
- Wei‐Hsuan Lo‐Ciganic (ORCID: https://orcid.org/0000-0001-6590-4770)
- Julie M. Donohue
- Lingshu Xue (ORCID: https://orcid.org/0000-0001-8226-6272)
Institutions
- University of Pittsburgh (US)
Publication Details
- Journal
- Addiction
- Published
- 2025-04-29
- DOI
- https://doi.org/10.1111/add.70079
- Citations
- 1
- Primary Topic
- Opioid Use Disorder Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.88
Funders
- University of Pittsburgh