Machine learning-based approach to guide the choice between baricitinib and tocilizumab in critical COVID-19 pneumonia treatment: a retrospective cohort study
ML models may support treatment selection between BCT and TCZ in patients with critical COVID-19 pneumonia. Prospective studies are warranted to assess whether model-guided choices improve survival and to validate generalizability across clinical settings.
Authors
- Hyeon Mu Jang (ORCID: https://orcid.org/0009-0003-0132-5261)
- K KWON (ORCID: https://orcid.org/0000-0003-2954-3888)
- E. J. Chang
- Yong Chong (ORCID: https://orcid.org/0000-0002-8804-3181)
- Su‐Yeon Choi (ORCID: https://orcid.org/0000-0001-9977-4740)
- So Yun Lim (ORCID: https://orcid.org/0000-0002-5647-3718)
- Gyucheol Choi (ORCID: https://orcid.org/0000-0002-1479-6236)
- Min Jae Kim (ORCID: https://orcid.org/0000-0002-5489-8608)
- Jiwon Jung
- Seongman Bae
- Sung-Han Kim
- Yang Soo Kim
- Yoon Kyung Park
- Sungwon Lim
- Jamin Koo
- Myung-Soo Kim
- Sang-Oh Lee
Institutions
- Ulsan College (KR)
- Asan Medical Center (KR)
- University of Ulsan (KR)
- DongKook Pharmaceutical (South Korea) (KR)
- nView Medical (United States) (US)
- Hanyang University (KR)
- Hongik University (KR)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2026-01-07
- DOI
- https://doi.org/10.3389/fmed.2025.1734109
- Primary Topic
- COVID-19 Clinical Research Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Research Foundation
- Hongik University
- Ministry of Science and ICT, South Korea