机器学习在HBV感染相关疾病中的应用

芮法娟; 薛旗; 刘翠红; 郭朝阳; 杨红丽; 刘传礼; 徐琊芸; 任万华; 秦成勇; 李婕

doi:10.3969/j.issn.1001-5256.2021.07.044

机器学习在HBV感染相关疾病中的应用

DOI: 10.3969/j.issn.1001-5256.2021.07.044

芮法娟^1a,
薛旗^1a,
刘翠红^1b,
郭朝阳^1a,
杨红丽²,
刘传礼^1a,
徐琊芸²,
任万华^1a,
秦成勇²,
李婕^{1a, 2, ,}

1a.
山东第一医科大学附属省立医院感染性疾病科，济南250021
1b.
山东第一医科大学附属省立医院超声诊疗科，济南250021
2.
山东大学附属省立医院感染性疾病科，济南 250021

基金项目:

国家自然科学基金面上项目 (81970545);

国家“十三五”科技重大专项项目 (2018ZX10302206－001－006);

山东省重点研发计划项目 (2019GSF108145)

利益冲突声明：所有作者均声明不存在利益冲突。

作者贡献声明：芮法娟负责文献检索，资料分析，撰写论文; 薛旗、刘翠红、郭朝阳、杨红丽、刘传礼、徐琊芸参与收集数据，修改论文; 任万华、秦成勇参与论文修改; 李婕负责课题设计，拟定写作思路，指导撰写文章并最后定稿。

详细信息

通信作者:
李婕，lijier@sina.com

中图分类号: R512.62
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- 被引次数: 0
出版历程
- 收稿日期: 2020-12-05
- 录用日期: 2021-01-11
- 出版日期: 2021-07-20

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

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)

摘要

摘要: 近年来，机器学习在医学领域的应用越来越广泛。在乳腺癌、糖尿病视网膜病变、神经精神疾病、动脉粥样硬化诊断和治疗方面均有新的进展。机器学习在肝病的诊断和预测方面具有很大潜力。结合患者血清学指标及影像学结果，利用机器学习的方法构建HBV相关肝病的诊断、预测模型，得到广泛的认可。旨在介绍机器学习方法在HBV相关肝病中的应用、现状、优点和进展。
- 机器学习 /
- 乙型肝炎病毒 /
- 肝疾病
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

HTML全文

表 1 常见的算法列表的应用及优缺点

名称	应用	优缺点
SVM^{[3-4, 10]}	是一种强大的分类工具，对数据进行二元分类的广义线性分类器，可以用于具有许多变量或维度的复杂数据集	优点：分离数据，最大化分离边际，提高了分类的敏感度和特异度，降低误分类率; 缺点：只考虑边缘的最大化而忽略了半径的最小化
DT^[11-12]	用于分类和回归的非参数监督学习方法，可以处理数值数据和分类数据	优点：可以对训练集产生良好的预测; 缺点：有许多分割树可能会过度拟合模型，从而导致测试集性能较差
RF^{[8, 12]}	用于分类和回归的典型集成学习方法	优点：可以弱化了单个决策树分类器经常出现的过拟合问题，对特征向量包含离散值的情况具有鲁棒性; 缺点：大量的树使算法速度慢，对实时预测无效
NB^{[5, 8]}	基于先验概率和观察到的训练集，通过计算最大后验概率预测给定样本分类	优点：结合先验概率和后验概率，避免了先验概率的主观偏见也避免了单独使用样本信息的过拟合线性; 缺点：先验概率很多时候取决于假设，可能导致预测模型不佳
LR^{[5, 12]}	广泛用于二元因变量的建模	优点：计算量与特征的数目相关，简单易理解，训练速度快; 缺点：形式简单，其准确率不高
KNN^[13-14]	广泛用于大数据分类，数据分布只涉及少量或不涉及先验知识的分类研究，KNN都是最佳选择	优点：精度高，简单好用，容易理解; 缺点：计算复杂性高，空间复杂性高，没有原则性的方法来选择使用的最近邻数，K值过高或过低都会产生不利的假阳性率或假阴性率
MLP^{[6, 15]}	是一种非参数动态模型，用于分类和回归	优点：可以使用大量的简单单元并行处理信息，并能够区分线性不可分的数据; 缺点：容易过拟合，参数难以调试，梯度弥散

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