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_46
Snippet: Similarity in AUCs, sensitivity and specificity for RF and LR models also demonstrated the robustness, according to prior study that classification method showed most dominant in variability of model. 11 The study was limited by small sample size. The percentage of short-term hospital stay is low in our multicenter cohorts, and semi-automated lesion segmentation might result in selection bias. A larger prospective multicenter cohort is needed to .....
Document: Similarity in AUCs, sensitivity and specificity for RF and LR models also demonstrated the robustness, according to prior study that classification method showed most dominant in variability of model. 11 The study was limited by small sample size. The percentage of short-term hospital stay is low in our multicenter cohorts, and semi-automated lesion segmentation might result in selection bias. A larger prospective multicenter cohort is needed to tune and test the machine learning-based CT radiomics models.
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