An interpretable machine learning model for prediction of significant liver fibrosis in comorbid chronic hepatitis B and nonalcoholic fatty liver disease: a retrospective development and validation study

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Journal
BMC Gastroenterology
Published
2026-01-16
DOI
https://doi.org/10.1186/s12876-025-04594-4
Primary Topic
Liver Disease Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00
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article

An interpretable machine learning model for prediction of significant liver fibrosis in comorbid chronic hepatitis B and nonalcoholic fatty liver disease: a retrospective development and validation study

JiaPing GU, Jiale Chai
BMC Gastroenterology
Liver Disease Diagnosis and Treatment
article

An interpretable machine learning model for prediction of significant liver fibrosis in comorbid chronic hepatitis B and nonalcoholic fatty liver disease: a retrospective development and validation study

JiaPing GU, Jiale Chai
article en

Abstract

No abstract available for this paper.
BMC GastroenterologyVol. 26(1)
Affiliated Hangzhou First People's Hospital, Westlake University, School of Medicine (CN)
Peace, Justice and strong institutions
Openalex Percentile: Top 86%
Liver Disease Diagnosis and Treatment
DOIOpen Access PDFOpenAlex
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An interpretable machine learning model for prediction of significant liver fibrosis in comorbid chronic hepatitis B and nonalcoholic fatty liver disease: a retrospective development and validation study — JiaPing GU, Jiale Chai · BMC Gastroenterology (2026) | TGRS Research Map | TGRS