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_12
Snippet: All the statistical analysis was performed using SPSS (Version 26) with statistical significance set at 0.05. Statistical optimization of the deep learning model was done through iterative training using Python (Version 3.6 with scipy, scikit-learn, and pytorch packages). The differences of clinical and laboratory data and imaging features between the patient with and without severe/critical progression were compared using Chi-square test, Fisher.....
Document: All the statistical analysis was performed using SPSS (Version 26) with statistical significance set at 0.05. Statistical optimization of the deep learning model was done through iterative training using Python (Version 3.6 with scipy, scikit-learn, and pytorch packages). The differences of clinical and laboratory data and imaging features between the patient with and without severe/critical progression were compared using Chi-square test, Fisher's exact test, independent t test and paired t test.
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