Machine learning based early warning system enables accurate mortality risk prediction for COVID-19

Gao, Yue; Cai, Guang-Yao; Fang, Wei; Li, Hua-Yi; Wang, Si-Yuan; Chen, Lingxi; Yu, Yang; Liu, Dan; Xu, Sen; Cui, Peng-Fei; Zeng, Shao-Qing; Feng, Xin-Xia; Yu, Rui-Di; Wang, Ya; Yuan, Yuan; Jiao, Xiao-Fei; Chi, Jian-Hua; Liu, Jia-Hao; Li, Ru-Yuan; Zheng, Xu; Song, Chun-Yan; Jin, Ning; Gong, Wen-Jian; Liu, Xing-Yu; Huang, Lei; Tian, Xun; Li, Lin; Xing, Hui; Ma, Ding; Li, Chun-Rui; Ye, Fei; Gao, Qing-Lei

Soaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed health facilities have endeavored to mitigate the pandemic, but mortality of COVID-19 continues to increase. Here, we present a mortality risk prediction model for COVID-19 (MRPMC) that uses patients’ clinical data on admission to stratify patients by mortality risk, which enables prediction of physiological deterioration and death up to 20 days in advance. This ensemble model is built using four machine learning methods including Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, and Neural Network. We validate MRPMC in an internal validation cohort and two external validation cohorts, where it achieves an AUC of 0.9621 (95% CI: 0.9464–0.9778), 0.9760 (0.9613–0.9906), and 0.9246 (0.8763–0.9729), respectively. This model enables expeditious and accurate mortality risk stratification of patients with COVID-19, and potentially facilitates more responsive health systems that are conducive to high risk COVID-19 patients.