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_20
Snippet: We conducted comprehensive experiments to validate our hypotheses and compared the performance of various models. Table 4 summarized the performance of traditional multi-stage and deep learning-based methods. Static clinical data including personal information, dynamic quantitative chest CT data or both of them were used for predictive experiments......
Document: We conducted comprehensive experiments to validate our hypotheses and compared the performance of various models. Table 4 summarized the performance of traditional multi-stage and deep learning-based methods. Static clinical data including personal information, dynamic quantitative chest CT data or both of them were used for predictive experiments.
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