FIGURE 1.

Feature selection by LASSO and model performance across cohorts. A, LASSO variable trace profiles of the eight features. The vertical dashed line shows the best lambda value (0.035) chosen by 10‐fold cross validation. B, Feature with zero coefficient (colored with gray) at lambda = 0.035 was considered less crucial to the patient's critical illness status and removed by Lasso logistic regression analysis. Feature with positive coefficient (colored with red) is regarded high risk in respect to critical illness. C, D, ROC curve and AUC of SVM, LR, GBDT, KNN, and NN in SFV cohort and OV cohort, respectively. E, F, KM curve of low‐risk and high‐risk subgroups predicted by SVM model in SFV and OV cohorts, respectively. The light red or blue areas refer to the 95% confidence interval. P value is computed by log‐rank test. Hazard ratio (HR) and its 95% confidence interval are obtained with univariate Cox model. Abbreviations: CRP, C reactive protein; GBDT, gradient boosted decision tree; HR, hazard ratio; IL‐2R, interleukin 2 receptor; IL‐6, interleukin 6; IL‐8, interleukin 8; IL‐10, interleukin 10; IL‐1β, interleukin 1β; KNN, k‐nearest neighbor; LASSO, least absolute shrinkage and selection operator; LR, logistic regression; NN, neural network; OV cohort, external validation cohort of Optical Valley Campus of Tongji Hospital; SFV cohort, internal validation cohort of Sino‐French New City Campus of Tongji Hospital; SVM, supported vector machine; TNF‐α, tumor necrosis factor α