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Machine learning-based predictive and risk analysis using real-world data with blood biomarkers for hepatitis B patients in the malignant progression of hepatocellular carcinoma
Hepatitis B Virus (HBV) infection may lead to various liver diseases such as cirrhosis, end-stage liver complications, and Hepatocellular carcinoma (HCC). Patients with existing cirrhosis or severe fibrosis have an increased chance of developing HCC. Consequently, lifetime observation is currently a...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9791942/ https://www.ncbi.nlm.nih.gov/pubmed/36578484 http://dx.doi.org/10.3389/fimmu.2022.1031400 |
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author | Nan, Yuemin Zhao, Suxian Zhang, Xiaoxiao Xiao, Zhifeng Guo, Ruihan |
author_facet | Nan, Yuemin Zhao, Suxian Zhang, Xiaoxiao Xiao, Zhifeng Guo, Ruihan |
author_sort | Nan, Yuemin |
collection | PubMed |
description | Hepatitis B Virus (HBV) infection may lead to various liver diseases such as cirrhosis, end-stage liver complications, and Hepatocellular carcinoma (HCC). Patients with existing cirrhosis or severe fibrosis have an increased chance of developing HCC. Consequently, lifetime observation is currently advised. This study gathered real-world electronic health record (EHR) data from the China Registry of Hepatitis B (CR-HepB) database. A collection of 396 patients with HBV infection at different stages were obtained, including 1) patients with a sustained virological response (SVR), 2) patients with HBV chronic infection and without further development, 3) patients with cirrhosis, and 4) patients with HCC. Each patient has been monitored periodically, yielding multiple visit records, each is described using forty blood biomarkers. These records can be utilized to train predictive models. Specifically, we develop three machine learning (ML)-based models for three learning tasks, including 1) an SVR risk model for HBV patients via a survival analysis model, 2) a risk model to encode the progression from HBV, cirrhosis and HCC using dimension reduction and clustering techniques, and 3) a classifier to detect HCC using the visit records with high accuracy (over 95%). Our study shows the potential of offering a comprehensive understanding of HBV progression via predictive analysis and identifies the most indicative blood biomarkers, which may serve as biomarkers that can be used for immunotherapy. |
format | Online Article Text |
id | pubmed-9791942 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97919422022-12-27 Machine learning-based predictive and risk analysis using real-world data with blood biomarkers for hepatitis B patients in the malignant progression of hepatocellular carcinoma Nan, Yuemin Zhao, Suxian Zhang, Xiaoxiao Xiao, Zhifeng Guo, Ruihan Front Immunol Immunology Hepatitis B Virus (HBV) infection may lead to various liver diseases such as cirrhosis, end-stage liver complications, and Hepatocellular carcinoma (HCC). Patients with existing cirrhosis or severe fibrosis have an increased chance of developing HCC. Consequently, lifetime observation is currently advised. This study gathered real-world electronic health record (EHR) data from the China Registry of Hepatitis B (CR-HepB) database. A collection of 396 patients with HBV infection at different stages were obtained, including 1) patients with a sustained virological response (SVR), 2) patients with HBV chronic infection and without further development, 3) patients with cirrhosis, and 4) patients with HCC. Each patient has been monitored periodically, yielding multiple visit records, each is described using forty blood biomarkers. These records can be utilized to train predictive models. Specifically, we develop three machine learning (ML)-based models for three learning tasks, including 1) an SVR risk model for HBV patients via a survival analysis model, 2) a risk model to encode the progression from HBV, cirrhosis and HCC using dimension reduction and clustering techniques, and 3) a classifier to detect HCC using the visit records with high accuracy (over 95%). Our study shows the potential of offering a comprehensive understanding of HBV progression via predictive analysis and identifies the most indicative blood biomarkers, which may serve as biomarkers that can be used for immunotherapy. Frontiers Media S.A. 2022-12-12 /pmc/articles/PMC9791942/ /pubmed/36578484 http://dx.doi.org/10.3389/fimmu.2022.1031400 Text en Copyright © 2022 Nan, Zhao, Zhang, Xiao and Guo https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Immunology Nan, Yuemin Zhao, Suxian Zhang, Xiaoxiao Xiao, Zhifeng Guo, Ruihan Machine learning-based predictive and risk analysis using real-world data with blood biomarkers for hepatitis B patients in the malignant progression of hepatocellular carcinoma |
title | Machine learning-based predictive and risk analysis using real-world data with blood biomarkers for hepatitis B patients in the malignant progression of hepatocellular carcinoma |
title_full | Machine learning-based predictive and risk analysis using real-world data with blood biomarkers for hepatitis B patients in the malignant progression of hepatocellular carcinoma |
title_fullStr | Machine learning-based predictive and risk analysis using real-world data with blood biomarkers for hepatitis B patients in the malignant progression of hepatocellular carcinoma |
title_full_unstemmed | Machine learning-based predictive and risk analysis using real-world data with blood biomarkers for hepatitis B patients in the malignant progression of hepatocellular carcinoma |
title_short | Machine learning-based predictive and risk analysis using real-world data with blood biomarkers for hepatitis B patients in the malignant progression of hepatocellular carcinoma |
title_sort | machine learning-based predictive and risk analysis using real-world data with blood biomarkers for hepatitis b patients in the malignant progression of hepatocellular carcinoma |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9791942/ https://www.ncbi.nlm.nih.gov/pubmed/36578484 http://dx.doi.org/10.3389/fimmu.2022.1031400 |
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