Machine learning-based models for advanced fibrosis in non-alcoholic steatohepatitis patients: A cohort study
The XGBoost model provides a highly accurate, non-invasive diagnosis of advanced liver fibrosis in NASH patients, outperforming traditional methods. An online tool based on this model has been developed to assist clinicians in evaluating the risk of advanced liver fibrosis.
Authors
- Lei Sun (ORCID: https://orcid.org/0000-0001-7230-7640)
- Xunming Ji (ORCID: https://orcid.org/0000-0003-0293-2744)
- Yixin Hou (ORCID: https://orcid.org/0000-0001-8233-7210)
- Peipei Meng (ORCID: https://orcid.org/0000-0003-0327-6099)
- Yang Zhou (ORCID: https://orcid.org/0000-0001-9273-0489)
- Xuejie Zhang (ORCID: https://orcid.org/0000-0002-4029-7000)
- Tong Wu (ORCID: https://orcid.org/0000-0001-5167-1742)
- Feng Xiong (ORCID: https://orcid.org/0000-0002-8173-8845)
- Xianbo Wang (ORCID: https://orcid.org/0000-0002-3593-5741)
- Jialiang Chen (ORCID: https://orcid.org/0009-0009-1674-7954)
Institutions
- Capital Medical University (CN)
- Beijing Ditan Hospital (CN)
Publication Details
- Journal
- World Journal of Gastroenterology
- Published
- 2025-02-18
- DOI
- https://doi.org/10.3748/wjg.v31.i9.101383
- Citations
- 6
- Primary Topic
- Liver Disease Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 8.13