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November 23, 2025Virology JournalOpen Access

Machine learning model for HBsAg seroclearance after 48-week pegylated interferon therapy in inactive HBsAg carriers: a retrospective study

Jianxia Dong Jing Zhao Haitian Yu Yao Xie Junliang Fu Xiaorong Mao Zhiliang Gao Bingliang Lin Yongfang Jiang Xiulan Xue Yueyong Zhu Haidong Zhao Haifang Cao Xinyue Chen

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Abstract

The RF-based model accurately predicts HBsAg clearance in IHC patients undergoing Peg-IFN therapy and offers a promising tool for early identification of candidates for individualized treatment strategies.

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Cite This Study

Dong et al. (2025) studied this question.

synapsesocial.com/papers/69403fa32d562116f290e3f3 https://doi.org/10.1186/s12985-025-03012-1

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Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Machine learning prediction of rapid HBsAg seroclearance at week 24 in inactive carriers treated with pegylated interferon2025
2A 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 242025
3Noninvasive models for patient selection and Early response monitoring of interferon therapy for HBsAg clearance in patients with chronic hepatitis BOriginal Research · 2026
4HBsAg seroreversion after pegylated interferon alpha induced HBsAg seroclearance in patients with chronic hepatitis B: a real-world cohort studyCohort Study · 2026
5Predictive model for HBsAg clearance rate in chronic hepatitis B patients treated with pegylated interferon α-2b for 48 weeksCohort Study · 2024 · 9 citations