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_29
Snippet: The cutoff point was defined on receiver operating characteristic (ROC) curves of training data by maximizing the sum of sensitivity and specificity. The model performance was evaluated using test dataset on lung lobe-level. Areas under the ROC curve (AUC), sensitivities, specificities, positive predictive value (PPV), and negative predictive value (NPV) were recorded. On patient-level, one was defined as long-term hospital stay once more than on.....
Document: The cutoff point was defined on receiver operating characteristic (ROC) curves of training data by maximizing the sum of sensitivity and specificity. The model performance was evaluated using test dataset on lung lobe-level. Areas under the ROC curve (AUC), sensitivities, specificities, positive predictive value (PPV), and negative predictive value (NPV) were recorded. On patient-level, one was defined as long-term hospital stay once more than one lesion of lung lobe was labeled as long-term stay lesion, if not, as short-term hospital stay.
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