Machine learning-based prediction of response to Janus kinase inhibitors in patients with rheumatoid arthritis using clinical data
The findings suggest that ML models can accurately predict treatment response to Janus kinase inhibitors in rheumatoid arthritis and may support personalized therapy selection to improve clinical outcomes.
Authors
- Yong‐Gil Kim (ORCID: https://orcid.org/0000-0002-8029-7355)
- Heeju Choi
- Yeo‐Jin Lee (ORCID: https://orcid.org/0000-0002-1050-673X)
- Jamin Koo (ORCID: https://orcid.org/0000-0003-4299-1546)
- Gyucheol Choi (ORCID: https://orcid.org/0000-0002-1479-6236)
- Jong Hyun Baek (ORCID: https://orcid.org/0000-0001-6430-0035)
- Kyung Hwan Kim (ORCID: https://orcid.org/0000-0002-2718-8758)
- Justin Jui Yuan Yeo (ORCID: https://orcid.org/0000-0001-9860-0992)
- Bo Young Kim (ORCID: https://orcid.org/0000-0001-9442-3300)
Institutions
- University of Ulsan (KR)
- Gangneung Asan Hospital (KR)
- DongKook Pharmaceutical (South Korea) (KR)
- nView Medical (United States) (US)
- Coherent (United States) (US)
- Hongik University (KR)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2025-11-26
- DOI
- https://doi.org/10.3389/fimmu.2025.1689144
- Primary Topic
- Rheumatoid Arthritis Research and Therapies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Hongik University