Author: Jiao Gong; Jingyi Ou; Xueping Qiu; Yusheng Jie; Yaqiong Chen; Lianxiong Yuan; Jing Cao; Mingkai Tan; Wenxiong Xu; Fang Zheng; Yaling Shi; Bo Hu
Title: A Tool to Early Predict Severe 2019-Novel Coronavirus Pneumonia (COVID-19) : A Multicenter Study using the Risk Nomogram in Wuhan and Guangdong, China Document date: 2020_3_20
ID: 51b7hss1_28
Snippet: The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.03. 17.20037515 doi: medRxiv preprint significant differences in age, sex, disease types between the train cohort and validation cohorts (Table 1 ). In the train cohort, the non-severe COVID-19 group consisted of 159 (86.41%) patients, with a median age of 45 years of age (range 33-61 years) while 25 patients (13.59%), with a median age of 6.....
Document: The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.03. 17.20037515 doi: medRxiv preprint significant differences in age, sex, disease types between the train cohort and validation cohorts (Table 1 ). In the train cohort, the non-severe COVID-19 group consisted of 159 (86.41%) patients, with a median age of 45 years of age (range 33-61 years) while 25 patients (13.59%), with a median age of 64 years of age (range 55-72 years) progressed to severe COVID-19. By the end of Table 2 . and support vector machine (SVM), and evaluated their performance by the receiver operating characteristic curve (ROC) and the precision-recall curve (appendix p1). There were no big difference in performance of these models except for decision tree. Therefore, logistic regression model was used for further analysis owing to its high predictive power and interpretability.
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