A machine learning-based predictive model for 48-week hepatitis B surface antigen seroclearance in chronic hepatitis B patients treated with pegylated interferon α-2b: prediction at week 24
The Random Forest model effectively predicted the 48-week HBsAg seroclearance rate using indicators measured at 24 weeks of PEG-IFNα-2b therapy. This model can provide a reliable reference for optimizing clinical treatment strategies.
Authors
- Shike Lou
- Lihong Qu (ORCID: https://orcid.org/0000-0001-8631-7882)
- Lang Zhou (ORCID: https://orcid.org/0000-0003-1443-4531)
- Kaixia Wang
- Nan Kong (ORCID: https://orcid.org/0000-0002-4047-3414)
- Yiling Wang (ORCID: https://orcid.org/0000-0001-5380-6022)
- Tao Wang (ORCID: https://orcid.org/0000-0002-1789-9767)
- Zhili Tan
Institutions
- Shanghai East Hospital (CN)
Publication Details
- Journal
- Frontiers in Cell and Developmental Biology
- Published
- 2025-11-26
- DOI
- https://doi.org/10.3389/fcell.2025.1734654
- Primary Topic
- Hepatitis B Virus Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Shanghai Pudong New Area Health Commission