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_18
Snippet: Chest CT is recommended as a routine test in the diagnoses and monitoring of COVID-19 since ground-glass opacities and consolidation are the most relative imaging features in pneumonia associated with SARS-CoV-2 infection. [4] [5] [6] On the basis of our previous work utilizing quantitative CT for COVID-19, 7 we hypothesized that high-throughout information hidden behind CT images 8 had potential in discriminating the hospital stay. The study aim.....
Document: Chest CT is recommended as a routine test in the diagnoses and monitoring of COVID-19 since ground-glass opacities and consolidation are the most relative imaging features in pneumonia associated with SARS-CoV-2 infection. [4] [5] [6] On the basis of our previous work utilizing quantitative CT for COVID-19, 7 we hypothesized that high-throughout information hidden behind CT images 8 had potential in discriminating the hospital stay. The study aimed to develop and test machine learning-based CT radiomics models for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection. All rights reserved. No reuse allowed without permission. author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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