Identifying tuberculous pleural effusion using artificial intelligence machine learning algorithms

被引:0
作者
Zenghua Ren
Yudan Hu
Ling Xu
机构
[1] Shanghai Jiao Tong University Affiliated Sixth People’s Hospital,Department of Respiratory Medicine
来源
Respiratory Research | / 20卷
关键词
Tuberculous pleural effusion; Diagnostic model; Artificial intelligence; Machine learning algorithm;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
相关论文
共 54 条
[1]  
Ryan H(2017)Corticosteroids for tuberculous pleurisy Cochrane Database Syst Rev 3 1753466618808660d-893
[2]  
Yoo J(2018)Biomarkers in the diagnosis of pleural diseases: a 2018 update Ther Adv Respir Dis 12 888-E494
[3]  
Darsini P(2016)Clinical and laboratory differences between lymphocyte- and neutrophil-predominant pleural tuberculosis PLoS One 11 E486-343
[4]  
Porcel JM(2015)Combined detections of interleukin-33 and adenosine deaminase for diagnosis of tuberculous pleural effusion Int J Clin Exp Pathol 8 334-686
[5]  
Choi H(2016)Tuberculous pleural effusion J Thorac Dis 8 682-127
[6]  
Chon HR(2017)Predicting primary progressive aphasias with support vector machine approaches in structural MRI data Neuroimage Clin 14 122-30
[7]  
Kim K(2017)Development of machine learning models for diagnosis of glaucoma PLoS One 12 21-1369
[8]  
Li D(2013)Artificial neural network models to support the diagnosis of pleural tuberculosis in adult patients Int J Tuberc Lung Dis. 17 1363-154
[9]  
Shen Y(2016)Diagnostic strategies in the tuberculosis Clinic of the Hospital General La Raza National Medical Center Rev Med Inst Mex Seguro Soc 54 147-13158
[10]  
Fu X(2015)Diagnostic performance of different pleural fluid biomarkers in tuberculous pleurisy Adv Exp Med Biol 852 13132-151