机器学习在慢性丙型肝炎诊疗中的应用

韩华, 段钟平, 王扬

PDF(520 KB)
中国高校科技期刊信息汇聚平台
PDF(520 KB)
临床肝胆病杂志 ›› 2025, Vol. 41 ›› Issue (1) : 141-144. DOI: 10.12449/JCH250121
综述

机器学习在慢性丙型肝炎诊疗中的应用

作者信息 +

Application of machine learning in the diagnosis and treatment of chronic hepatitis C

Author information +
History +

摘要

随着人工智能技术的发展，机器学习在医疗健康领域中展现出巨大的应用潜力。机器学习通过对患者的临床特征、血液检验、影像学检查等数据进行综合分析，建立相应的数学模型，以实现对疾病的诊断、治疗及病情评估的预测，指导疾病的管理。本文结合最新的研究成果，综述了机器学习在慢性丙型肝炎中的应用情况及研究进展。

Abstract

With the development of artificial intelligence， machine learning has shown great potential in the field of medical health. Machine learning conducts a comprehensive analysis of patient data including clinical features， blood tests， and imaging examinations and establishes corresponding mathematical models to achieve the diagnosis and treatment of diseases and the prediction of disease conditions， thereby guiding disease management. With reference to the latest research findings， this article reviews the application of machine learning in chronic hepatitis C and related research advances.

关键词

丙型肝炎， 慢性 / 机器学习 / 诊断 / 治疗学

Key words

Hepatitis C， Chronic / Machine Learning / Diagnosis / Therapeutics

引用本文

EndNote

Ris (Procite)

Bibtex

导出引用
韩华 , 段钟平 , 王扬. 机器学习在慢性丙型肝炎诊疗中的应用. 临床肝胆病杂志. 2025, 41(1): 141-144 https://doi.org/10.12449/JCH250121
Hua HAN, Zhongping DUAN, Yang WANG. Application of machine learning in the diagnosis and treatment of chronic hepatitis C[J]. Journal of Clinical Hepatol. 2025, 41(1): 141-144 https://doi.org/10.12449/JCH250121

参考文献

列表( 原文顺序 | 文献年度倒序 | 文中引用次数倒序 ) 可视化分析
1
FARRAG AN, KAMEL AM, EL-BARAKY IA. Opportunities and challenges for the application of artificial intelligence paradigms into the management of endemic viral infections: The example of chronic hepatitis C virus[J]. Rev Med Virol, 2024, 34(2): e2514. DOI: 10.1002/rmv.2514 .
本文引用 [1]
2
BRUNNER N, BRUGGMANN P. Trends of the global hepatitis C disease burden: Strategies to achieve elimination[J]. J Prev Med Public Health, 2021, 54(4): 251-258. DOI: 10.3961/jpmph.21.151 .
本文引用 [1]
3
LE BERRE C, SANDBORN WJ, ARIDHI S, et al. Application of artificial intelligence to gastroenterology and hepatology[J]. Gastroenterology, 2020, 158(1): 76-94. DOI: 10.1053/j.gastro.2019.08.058 .
本文引用 [2]
4
NADIF M, ROLE F. Unsupervised and self-supervised deep learning approaches for biomedical text mining[J]. Brief Bioinform, 2021, 22(2): 1592-1603. DOI: 10.1093/bib/bbab016 .
本文引用 [1]
5
HASSABIS D, KUMARAN D, SUMMERFIELD C, et al. Neuroscience-inspired artificial intelligence[J]. Neuron, 2017, 95(2): 245-258. DOI: 10.1016/j.neuron.2017.06.011 .
本文引用 [1]
6
DOYLE OM, LEAVITT N, RIGG JA. Finding undiagnosed patients with hepatitis C infection: An application of artificial intelligence to patient claims data[J]. Sci Rep, 2020, 10(1): 10521. DOI: 10.1038/s41598-020-67013-6 .
本文引用 [1]
7
ELSHEWEY AM, SHAMS MY, TAWFEEK SM, et al. Optimizing HCV disease prediction in Egypt: The hyOPTGB framework[J]. Diagnostics, 2023, 13(22): 3439. DOI: 10.3390/diagnostics13223439 .
本文引用 [1]
8
REISER M, WIEBNER B, HIRSCH J, et al. Neural-network analysis of socio-medical data to identify predictors of undiagnosed hepatitis C virus infections in Germany (DETECT)[J]. J Transl Med, 2019, 17(1): 94. DOI: 10.1186/s12967-019-1832-4 .
本文引用 [1]
9
BUTARU AE, MĂMULEANU M, STREBA CT, et al. Resource management through artificial intelligence in screening programs-key for the successful elimination of hepatitis C[J]. Diagnostics, 2022, 12(2): 346. DOI: 10.3390/diagnostics12020346 .
本文引用 [1]
10
LI TH S, CHIU HJ, KUO PH. Hepatitis C virus detection model by using random forest, logistic-regression and ABC algorithm[J]. IEEE Access, 2022, 10: 91045-91058. DOI: 10.1109/ACCESS.2022.3202295 .
本文引用 [1]
11
ALIZARGAR A, CHANG YL, TAN TH. Performance comparison of machine learning approaches on hepatitis C prediction employing data mining techniques[J]. Bioengineering (Basel), 2023, 10(4): 481. DOI: 10.3390/bioengineering10040481 .
本文引用 [1]
12
FLAMM S, LAWITZ E, BORG B, et al. Efficacy and safety of sofosbuvir/velpatasvir plus ribavirin in patients with hepatitis C virus-related decompensated cirrhosis[J]. Viruses, 2023, 15(10): 2026. DOI: 10.3390/v15102026 .
本文引用 [1]
13
CHIRIKOV VV, MARX SE, MANTHENA SR, et al. Development of a comprehensive dataset of hepatitis C patients and examination of disease epidemiology in the United States, 2013-2016[J]. Adv Ther, 2018, 35(7): 1087-1102. DOI: 10.1007/s12325-018-0721-1 .
本文引用 [1]
14
CHURKIN A, KRISS S, UZIEL A, et al. Machine learning for mathematical models of HCV kinetics during antiviral therapy[J]. Math Biosci, 2022, 343: 108756. DOI: 10.1016/j.mbs.2021.108756 .
本文引用 [1]
15
PARK H, LO-CIGANIC WH, HUANG J, et al. Machine learning algorithms for predicting direct-acting antiviral treatment failure in chronic hepatitis C: An HCV-TARGET analysis[J]. Hepatology, 2022, 76(2): 483-491. DOI: 10.1002/hep.32347 .
本文引用 [1]
16
HAGA H, SATO H, KOSEKI A, et al. A machine learning-based treatment prediction model using whole genome variants of hepatitis C virus[J]. PLoS One, 2020, 15(11): e0242028. DOI: 10.1371/journal.pone.0242028 .
本文引用 [1]
17
JANCZEWSKA E, KOŁEK MF, LORENC B, et al. Factors influencing the failure of interferon-free therapy for chronic hepatitis C: Data from the Polish EpiTer-2 cohort study[J]. World J Gastroenterol, 2021, 27(18): 2177-2192. DOI: 10.3748/wjg.v27.i18.2177 .
本文引用 [1]
18
SHOUSHA HI, AWAD AH, OMRAN DA, et al. Data mining and machine learning algorithms using IL28B genotype and biochemical markers best predicted advanced liver fibrosis in chronic hepatitis C[J]. Jpn J Infect Dis, 2018, 71(1): 51-57. DOI: 10.7883/yoken.JJID.2017.089 .
本文引用 [1]
19
HASHEM S, ESMAT G, ELAKEL W, et al. Comparison of machine learning approaches for prediction of advanced liver fibrosis in chronic hepatitis C patients[J]. IEEE/ACM Trans Comput Biol Bioinform, 2018, 15(3): 861-868. DOI: 10.1109/TCBB.2017.2690848 .
本文引用 [1]
20
KONERMAN MA, ZHANG YW, ZHU J, et al. Improvement of predictive models of risk of disease progression in chronic hepatitis C by incorporating longitudinal data[J]. Hepatology, 2015, 61(6): 1832-1841. DOI: 10.1002/hep.27750 .
本文引用 [1]
21
IOANNOU GN, TANG WJ, BESTE LA, et al. Assessment of a deep learning model to predict hepatocellular carcinoma in patients with hepatitis C cirrhosis[J]. JAMA Netw Open, 2020, 3(9): e2015626. DOI: 10.1001/jamanetworkopen.2020.15626 .
本文引用 [1]

作者贡献声明

王扬负责课题设计，资料分析，拟定写作思路；韩华负责查阅文献，撰写论文，修改论文；段钟平负责指导文章撰写并最后定稿。

评论

PDF(520 KB)

Accesses

Citation

Detail
段落导航
相关文章
Copyright 中国高校科技期刊信息汇聚平台 2024-2025
技术支持：北京玛格泰克科技发展有限公司
京ICP备05021913号-19

/

〈 〉