Application of machine learning in hepatitis B virus-related liver diseases

Fajuan RUI; Qi XUE; Cuihong LIU; Zhaoyang GUO; Hongli YANG; Chuanli LIU; Yayun XU; Wanhua REN; Chengyong QIN; Jie LI

doi:10.3969/j.issn.1001-5256.2021.07.044

Volume 37 Issue 7

Jul. 2021

Turn off MathJax

Article Contents

Article Navigation > Journal of Clinical Hepatology > 2021 > 37(7): 1686-1689

PDF( 1956 KB)

Application of machine learning in hepatitis B virus-related liver diseases

DOI: 10.3969/j.issn.1001-5256.2021.07.044

1a.
Department of Infectious Disease, Shandong Provincial Hospital Affiliated to Shandong Frist Medical University, Jinan 250021, China
1b.
Department of Ultrasound Diagnosis and Treatment, Shandong Provincial Hospital Affiliated to Shandong Frist Medical University, Jinan 250021, China
2.
Department of Infectious Disease, Shandong Provincial Hospital Affiliated to Shandong University, Jinan 250021, China

Research funding:

National Natural Science Foundation of China(General Program) (81970545);

National Science and Technology Major Project of China (2018ZX10302206－001－006);

Shandong Province Key and Development Project (2019GSF108145)

Received Date: 2020-12-05
Accepted Date: 2021-01-11
Published Date: 2021-07-20

Abstract

Abstract

Machine learning has been more and more widely used in the medical field in recent years, and new advances have been made in the diagnosis and treatment of breast cancer, diabetic retinopathy, neuropsychiatric diseases, and atherosclerosis. Machine learning is showing great potential in the diagnosis and prediction of liver diseases. With reference to patients' serological markers and imaging findings, the model established based on machine learning for the diagnosis and prediction of hepatitis B virus (HBV)-related liver diseases has been widely recognized. This article introduces the application, current status, advantages, and advances of machine learning in HBV-related liver diseases.
- Machine Learning,
- Hepatitis B Virus,
- Liver Diseases

FullText(HTML)

References(35)

References

[1]	Chinese Society of Infectious Diseases, Chinese Medical Association; Chinese Society of Hepatology, Chinese Medical Association. Guidelines for the prevention and treatment of chronic hepatitis B (version 2019)[J]. J Clin Hepatol, 2019, 35(12): 2648-2669. DOI: 10.3969/1.issn. 1001-5256.2019.12.007. 中华医学会感人病学分会, 中华医学会肝病学分会. 慢性乙型肝炎防治指南(2019年版)[J]. 临床肝胆病杂志, 2019, 35(12): 2648-2669. DOI: 10.3969/1.issn.1001-5256.2019.12.007.
[2]	LI W, HUANG Y, ZHUANG BW, et al. Multiparametric ultrasomics of significant liver fibrosis: A machine learning-based analysis[J]. Eur Radiol, 2019, 29(3): 1496-1506. DOI: 10.1007/s00330-018-5680-z.
[3]	HUANG S, CAI N, PACHECO PP, et al. Applications of Support Vector Machine (SVM) learning in cancer genomics[J]. Cancer Genomics Proteomics, 2018, 15(1): 41-51. DOI: 10.21873/cgp.20063.
[4]	HANDELMAN GS, KOK HK, CHANDRA RV, et al. eDoctor: Machine learning and the future of medicine[J]. J Intern Med, 2018, 284(6): 603-619. DOI: 10.1111/joim.12822.
[5]	DORADO-DÍAZ PI, SAMPEDRO-GÓMEZ J, VICENTE-PALACIOS V, et al. Applications of artificial intelligence in cardiology. The future is already here[J]. Rev Esp Cardiol (Engl Ed), 2019, 72(12): 1065-1075. DOI: 10.1016/j.rec.2019.05.014.
[6]	LAN X, WEI R, CAI HW, et al. Application of machine learning algorithm in medical field[J]. Chin Med Equipment J, 2019, 40 (3): 93-97. DOI: 10.19745/j.1003-8868.2019076. 兰欣, 卫荣, 蔡宏伟, 等. 机器学习算法在医疗领域中的应用[J]. 医疗卫生装备, 2019, 40 (3): 93-97. DOI: 10.19745/j.1003-8868.2019076.
[7]	LIANG ST, GUO MZ, ZHAO LL, et al. Survey on medical decision support systems based on machine learning[J]. Comput Eng Applicat, 2019, 55(19): 1-11. DOI: 10.3778/j.issn.1002-8331.1903-0485. 梁书彤, 郭茂祖, 赵玲玲. 基于机器学习的医疗决策支持系统综述[J]. 计算机工程与应用, 2019, 55 (19): 1-11. DOI: 10.3778/j.issn.1002-8331.1903-0485.
[8]	CHEN Y, LUO Y, HUANG W, et al. Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis B[J]. Comput Biol Med, 2017, 89: 18-23. DOI: 10.1016/j.compbiomed.2017.07.012.
[9]	MA H, XU CF, SHEN Z, et al. Application of machine learning techniques for clinical predictive modeling: A cross-sectional study on nonalcoholic fatty liver disease in China[J]. Biomed Res Int, 2018, 2018: 4304376. DOI: 10.1155/2018/4304376.
[10]	WU X, ZUO W, LIN L, et al. F-SVM: Combination of feature transformation and SVM learning via convex relaxation[J]. IEEE Trans Neural Netw Learn Syst, 2018, 29(11): 5185-5199. DOI: 10.1109/TNNLS.2018.2791507.
[11]	de SANTANA FB, BORGES NETO W, POPPI RJ. Random forest as one-class classifier and infrared spectroscopy for food adulteration detection[J]. Food Chem, 2019, 293: 323-332. DOI: 10.1016/j.foodchem.2019.04.073.
[12]	TIAN X, CHONG Y, HUANG Y, et al. Using machine learning algorithms to predict hepatitis B surface antigen seroclearance[J]. Comput Math Methods Med, 2019, 2019: 6915850. DOI: 10.1155/2019/6915850.
[13]	ABU ALFEILAT HA, HASSANAT A, LASASSMEH O, et al. Effects of distance measure choice on K-nearest neighbor classifier performance: A review[J]. Big Data, 2019, 7(4): 221-248. DOI: 10.1089/big.2018.0175.
[14]	LO YC, RENSI SE, TORNG W, et al. Machine learning in chemoinformatics and drug discovery[J]. Drug Discov Today, 2018, 23(8): 1538-1546. DOI: 10.1016/j.drudis.2018.05.010.
[15]	WANG N, CAO Y, SONG W, et al. Serum peptide pattern that differentially diagnoses hepatitis B virus-related hepatocellular carcinoma from liver cirrhosis[J]. J Gastroenterol Hepatol, 2014, 29(7): 1544-1550. DOI: 10.1111/jgh.12545.
[16]	KHAN S, ULLAH R, KHAN A, et al. Analysis of hepatitis B virus infection in blood sera using Raman spectroscopy and machine learning[J]. Photodiagnosis Photodyn Ther, 2018, 23: 89-93. DOI: 10.1016/j.pdpdt.2018.05.010.
[17]	MUELLER-BRECKENRIDGE AJ, GARCIA-ALCALDE F, WILDUM S, et al. Machine-learning based patient classification using Hepatitis B virus full-length genome quasispecies from Asian and European cohorts[J]. Sci Rep, 2019, 9(1): 18892. DOI: 10.1038/s41598-019-55445-8.
[18]	ZHOU W, MA Y, ZHANG J, et al. Predictive model for inflammation grades of chronic hepatitis B: Large-scale analysis of clinical parameters and gene expressions[J]. Liver Int, 2017, 37(11): 1632-1641. DOI: 10.1111/liv.13427.
[19]	CAO Y, HE K, CHENG M, et al. Two classifiers based on serum peptide pattern for prediction of HBV-induced liver cirrhosis using MALDI-TOF MS[J]. Biomed Res Int, 2013, 2013: 814876. DOI: 10.1155/2013/814876.
[20]	ESLAM M, HASHEM AM, ROMERO-GOMEZ M, et al. FibroGENE: A gene-based model for staging liver fibrosis[J]. J Hepatol, 2016, 64(2): 390-398. DOI: 10.1016/j.jhep.2015.11.008.
[21]	WEI R, WANG J, WANG X, et al. Clinical prediction of HBV and HCV related hepatic fibrosis using machine learning[J]. EBioMedicine, 2018, 35: 124-132. DOI: 10.1016/j.ebiom.2018.07.041.
[22]	FU TT, YAO Z, DING H, et al. Computer-aided assessment of liver fibrosis progression in patients with chronic hepatitis B: An exploratory[J]. Natl Med J China, 2019, 99(7): 491-495. DOI: 10.3760/cma.j.issn.0376-2491.2019.07.003. 付甜甜, 姚钊, 丁红, 等. 计算机辅助诊断慢性乙肝患者肝纤维化进程的价值分析[J]. 中华医学杂志, 2019, 99 (7): 491-495. DOI: 10.3760/cma.j.issn.0376-2491.2019.07.003.
[23]	CAO Y, HU ZD, LIU XF, et al. An MLP classifier for prediction of HBV-induced liver cirrhosis using routinely available clinical parameters[J]. Dis Markers, 2013, 35(6): 653-660. DOI: 10.1155/2013/127962.
[24]	SANG C, XIE GX, LIANG DD, et al. Improvement of liver fibrosis diagnostic models based on Youden index[J]. J Shanghai Jiaotong Univ(Med Sci), 2019, 39(10): 1156-1161. DOI: 10.3969/j.issn.1674-8115.2019.10.009. 桑潮, 谢国祥, 梁丹丹, 等. 基于约登指数的肝纤维化诊断模型改进研究[J]. 上海交通大学学报(医学版), 2019, 39 (10): 1156-1161. DOI: 10.3969/j.issn.1674-8115.2019.10.009.
[25]	XIE G, WANG X, WEI R, et al. Serum metabolite profiles are associated with the presence of advanced liver fibrosis in Chinese patients with chronic hepatitis B viral infection[J]. BMC Med, 2020, 18(1): 144. DOI: 10.1186/s12916-020-01595-w.
[26]	ESTEVEZ J, CHEN VL, PODLAHA O, et al. Differential serum cytokine profiles in patients with chronic hepatitis B, C, and hepatocellular carcinoma[J]. Sci Rep, 2017, 7(1): 11867. DOI: 10.1038/s41598-017-11975-7.
[27]	LIAO H, XIONG T, PENG J, et al. Classification and prognosis prediction from histopathological images of hepatocellular carcinoma by a fully automated pipeline based on machine learning[J]. Ann Surg Oncol, 2020, 27(7): 2359-2369. DOI: 10.1245/s10434-019-08190-1.
[28]	TAO K, BIAN Z, ZHANG Q, et al. Machine learning-based genome-wide interrogation of somatic copy number aberrations in circulating tumor DNA for early detection of hepatocellular carcinoma[J]. EBioMedicine, 2020, 56: 102811. DOI: 10.1016/j.ebiom.2020.102811.
[29]	ZHAO RH, SHI Y, ZHAO H, et al. Acute-on-chronic liver failure in chronic hepatitis B: An update[J]. Expert Rev Gastroenterol Hepatol, 2018, 12(4): 341-350. DOI: 10.1080/17474124.2018.1426459.
[30]	SHI KQ, ZHOU YY, YAN HD, et al. Classification and regression tree analysis of acute-on-chronic hepatitis B liver failure: Seeing the forest for the trees[J]. J Viral Hepat, 2017, 24(2): 132-140. DOI: 10.1111/jvh.12617.
[31]	HERNAEZ R, SOLÀ E, MOREAU R, et al. Acute-on-chronic liver failure: An update[J]. Gut, 2017, 66(3): 541-553. DOI: 10.1136/gutjnl-2016-312670.
[32]	LI N, ZHENG RJ, JIE FR, et al. Analysis of the influencing factors of short-term mortality of HBV-ACLF and the establishment and comparison of prognosis models[J]. Chin Hepatol, 2019, 24 (12): 1399-1402. DOI: 10.14000/j.cnki.issn.1008-1704.2019.12.013. 李楠, 郑嵘炅, 揭方荣, 等. HBV-ACLF短期死亡影响因素分析及预后模型的建立与比较研究[J]. 肝脏, 2019, 24 (12): 1399-1402. DOI: 10.14000/j.cnki.issn.1008-1704.2019.12.013.
[33]	CUTILLO CM, SHARMA KR, FOSCHINI L, et al. Machine intelligence in healthcare-perspectives on trustworthiness, explainability, usability, and transparency[J]. NPJ Digit Med, 2020, 3: 47. DOI: 10.1038/s41746-020-0254-2.
[34]	AL'AREF SJ, ANCHOUCHE K, SINGH G, et al. Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging[J]. Eur Heart J, 2019, 40(24): 1975-1986. DOI: 10.1093/eurheartj/ehy404.
[35]	LUO ZW, CHEN X, ZHANG YF, et al. Application value of machine learning algorithms and COX nomogram in the survival prediction of hepatocellular carcinoma after resection[J]. Chin J Dig Surg, 2020, 19(2): 166-178. DOI: 10.3760/cma.j.issn.1673-9752.2020.02.009. 罗治文, 陈晓, 张业繁, 等. 机器学习算法和COX列线图在肝细胞癌术后生存预测中的应用价值[J]. 中华消化外科杂志, 2020, 19 (2): 166-178. DOI: 10.3760/cma.j.issn.1673-9752.2020.02.009.

Relative Articles

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Tables(1)

Get Citation

PDF

XML

Article Metrics

Article views (577) PDF downloads(54)

Application of machine learning in hepatitis B virus-related liver diseases

DOI: 10.3969/j.issn.1001-5256.2021.07.044

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Proportional views

Related

Application of machine learning in hepatitis B virus-related liver diseases

DOI: 10.3969/j.issn.1001-5256.2021.07.044

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Proportional views

Related

About Journal

Submission Guideline

Journal Online

Hepatobiliary College

Guidelines

Export File

Citation

Format

Content