Machine learning-based prediction models for liver-related events in patients with hepatitis B-related cirrhosis and clinically significant portal hypertension
Machine learning-based prediction models, particularly XGBoost and RF, can effectively identify high-risk individuals among patients with compensated hepatitis B virus-related cirrhosis and CSPH. LPR that incorporates LSM is a valuable and robust predictive indicator.
Authors
- Yanqiu Li (ORCID: https://orcid.org/0009-0008-8361-7116)
- Yongqi Li (ORCID: https://orcid.org/0000-0002-3907-2031)
- Zhuo-Jun Li
- Ying Feng
- Xian-Bo Wang
Institutions
- Beijing University of Posts and Telecommunications (CN)
- Capital Medical University (CN)
- Beijing Ditan Hospital (CN)
Publication Details
- Journal
- World Journal of Gastroenterology
- Published
- 2026-01-22
- DOI
- https://doi.org/10.3748/wjg.v32.i4.113492
- Primary Topic
- Liver Disease Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00