Author: Xiang Bai; Cong Fang; Yu Zhou; Song Bai; Zaiyi Liu; Qianlan Chen; Yongchao Xu; Tian Xia; Shi Gong; Xudong Xie; Dejia Song; Ronghui Du; Chunhua Zhou; Chengyang Chen; Dianer Nie; Dandan Tu; Changzheng Zhang; Xiaowu Liu; Lixin Qin; Weiwei Chen
Title: Predicting COVID-19 malignant progression with AI techniques Document date: 2020_3_23
ID: 50oy9qqy_23
Snippet: The copyright holder for this preprint (which was not peer-reviewed) is the The above results clearly supported the significance of complementary information from different medical data and time-series information from the chest CT sequence. Finally, our proposed method had a high probability of stabilizing at a high confidence interval, which is very important for clinical applications......
Document: The copyright holder for this preprint (which was not peer-reviewed) is the The above results clearly supported the significance of complementary information from different medical data and time-series information from the chest CT sequence. Finally, our proposed method had a high probability of stabilizing at a high confidence interval, which is very important for clinical applications.
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