Fig. 3: Predictive performance of models across cohorts.
From: Machine learning based early warning system enables accurate mortality risk prediction for COVID-19
AUC to assess the performance of mortality risk prediction of models (LR, SVM, GBDT, NN, and MRPMC) in a SFV cohort, b OV cohort, and c CHWH cohort, respectively. Source data are provided as a Source Data file. Kaplan–Meier curves indicating overall survival of patients with high and low mortality risk in d SFV cohort, e OV cohort, and f CHWH cohort, respectively. The tick marks refer to censored patients. The dark red or blue line indicates the survival probability, and the light red or blue areas represent the 95% confidence interval of survival probability (p < 0.0001). AUC area under the receiver operating characteristics curve, SFV internal validation cohort of Sino-French New City Campus of Tongji Hospital, OV Optical Valley Campus of Tongji Hospital, CHWH The Central Hospital of Wuhan, LR logistic regression, SVM support vector machine, GBDT gradient boosted decision tree, NN neural network, MRPMC mortality risk prediction model for COVID-19.
