Author: Xiaolong Qi; Zicheng Jiang; QIAN YU; Chuxiao Shao; Hongguang Zhang; Hongmei Yue; Baoyi Ma; Yuancheng Wang; Chuan Liu; Xiangpan Meng; Shan Huang; Jitao Wang; Dan Xu; Junqiang Lei; Guanghang Xie; Huihong Huang; Jie Yang; Jiansong Ji; Hongqiu Pan; Shengqiang Zou; Shenghong Ju
                    Title: Machine learning-based CT radiomics model for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: A multicenter study  Document date: 2020_3_3
                    ID: 2s4ifz7i_44
                    
                    Snippet: CT radiomics features hidden within lesions, ground-glass opacities and consolidation, were extracted, and machine-learning models demonstrated robust performance using multicenter cohorts for training and inter-validation, and an independent dataset and a prospective dataset for test......
                    
                    
                    
                     
                    
                    
                    
                    
                        
                            
                                Document: CT radiomics features hidden within lesions, ground-glass opacities and consolidation, were extracted, and machine-learning models demonstrated robust performance using multicenter cohorts for training and inter-validation, and an independent dataset and a prospective dataset for test.
 
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