Infection of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) could cause dramatic response in coronavirus disease 2019 (COVID‐19) patients at multi‐omics level,1-3 thus it is essential to systematically assess the pathogenesis of COVID‐19. In our previous study, we presented the first trans‐omics landscape of 236 COVID‐19 patients with 4 clinical severity groups (including asymptomatic, mild, severe and critically ill cases) and found that the mild and severe COVID‐19 patients shared several similar characteristics.4 However, it is crucial to discriminate mild from severe COVID‐19 patients to prevent the latter from the progression of disease by facilitating early intervention. Herein, we developed an extreme gradient boosting (XGBoost) machine‐learning model to predict the COVID‐19 severities by leveraging multi‐omics data. Briefly, we randomly stratified samples for the training set (80%) and the independent testing set (20%) (Figure 1A, see Methods in the Supporting Information).