Using machine learning algorithms to optimize treatment with high-cost biologics in a national cohort of patients with inflammatory bowel disease
Prediction models, particularly RF+, may aid in optimizing biologic therapy for CD and UC by identifying patients at higher risk of flare following a biosimilar switch.
Authors
- Tiffany M. Tang (ORCID: https://orcid.org/0000-0002-8079-6867)
- Francesca Cunningham (ORCID: https://orcid.org/0000-0001-7924-8372)
- Codey Pham
- Shubhada Sansgiry (ORCID: https://orcid.org/0000-0002-0089-496X)
- Akbar K. Waljee (ORCID: https://orcid.org/0000-0003-1964-8790)
- Jason K. Hou (ORCID: https://orcid.org/0000-0003-0644-6778)
- Jessica A Baker (ORCID: https://orcid.org/0009-0009-5100-1867)
- Tony Van
- Peter Richardson
- Ji Zhu
Institutions
- University of Notre Dame (US)
- Michael E. DeBakey VA Medical Center (US)
- Baylor College of Medicine (US)
- University of Michigan (US)
- Veterans Health Administration (US)
- VA Ann Arbor Healthcare System (US)
- VA Center for Clinical Management Research (US)
- Medication Management (United States) (US)
- University of Global Health Equity (RW)
- Department of Veterans Affairs (AU)
Publication Details
- Journal
- JAMIA Open
- Published
- 2025-11-03
- DOI
- https://doi.org/10.1093/jamiaopen/ooaf162
- Primary Topic
- Biosimilars and Bioanalytical Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00