Risk-Stratified Biologic Efficacy in Ulcerative Colitis: A Multicenter Machine Learning Study
Our random forest model enables precise risk stratification in UC, distinguishing patients with divergent responses to biologics. Low-risk patients derive significant benefit from timely biologics, while high-risk subgroups may require intensified strategies. This framework advances personalized UC management, though prospective validation is warranted.
Authors
- Zishan Liu (ORCID: https://orcid.org/0000-0002-1984-7397)
- Xinyu Liu (ORCID: https://orcid.org/0009-0006-3650-4931)
- Pingxin Zhang (ORCID: https://orcid.org/0000-0002-9064-9174)
- Chuhan Zhang (ORCID: https://orcid.org/0000-0001-8466-2797)
- Fengyuan Su (ORCID: https://orcid.org/0009-0009-0825-923X)
- Rui Feng (ORCID: https://orcid.org/0000-0002-4523-9344)
- Ren Mao
- Minhu Chen
- Shixian Hu
- Zicheng Huang
- Yun Qiu
- Liru Chen
- Yu Wang
- Kang Chao
Institutions
- Sun Yat-sen University (CN)
- Wuhan University (CN)
- Longgang Central Hospital (CN)
- Sixth Affiliated Hospital of Sun Yat-sen University (CN)
- Zhongnan Hospital of Wuhan University (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
Publication Details
- Journal
- Inflammatory Bowel Diseases
- Published
- 2025-10-20
- DOI
- https://doi.org/10.1093/ibd/izaf272
- Primary Topic
- Inflammatory Bowel Disease
- Type
- article
- Field-Weighted Citation Impact
- 0.00