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_31
Snippet: In conclusion, the deep learning-based method using clinical and quantitative CT data to predict malignant progression to severe/critical stage. We modeled the spatial information in the quantitative CT data and organized the static clinical data and dynamic chest CT data into a time series form. We validated the significance of complementary data and its special formatting form for this particular prediction task......
Document: In conclusion, the deep learning-based method using clinical and quantitative CT data to predict malignant progression to severe/critical stage. We modeled the spatial information in the quantitative CT data and organized the static clinical data and dynamic chest CT data into a time series form. We validated the significance of complementary data and its special formatting form for this particular prediction task.
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