Selected article for: "logistic regression and sensitivity specificity"
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_59
    Snippet: In the multicenter study with patients from 5 designated hospitals in China, the CT radiomics models using logistic regression and random forest method showed satisfied diagnostic performance with sensitivity of 1.0 and 0.75, specificity of 0.89 and 1.0, respectively, in independent test dataset......
    Document: In the multicenter study with patients from 5 designated hospitals in China, the CT radiomics models using logistic regression and random forest method showed satisfied diagnostic performance with sensitivity of 1.0 and 0.75, specificity of 0.89 and 1.0, respectively, in independent test dataset.

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