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_24
Snippet: To identify the relative importance of each feature, feature selection was performed using the least absolute shrinkage and selection operator (LASSO) regression method, and prediction models were built using logistic regression, decision tree, random forest (RF) and support vector machine (SVM) using R package Caret, using 300-time repeated random sub-sampling validation for diverse parameter conditions, respectively. As described previously, No.....
Document: To identify the relative importance of each feature, feature selection was performed using the least absolute shrinkage and selection operator (LASSO) regression method, and prediction models were built using logistic regression, decision tree, random forest (RF) and support vector machine (SVM) using R package Caret, using 300-time repeated random sub-sampling validation for diverse parameter conditions, respectively. As described previously, Nomograms were established with the rms package and the performance of nomogram was evaluated by discrimination (Harrell's concordance index) and calibration (calibration plots and Hosmer-Lemeshow calibration test) in R. During the external validation of the nomogram, the total points for each patient in the validation cohort were calculated based on the established nomogram.
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