An interpretable mortality prediction model for COVID-19 patients

Li Yan, Hai-Tao Zhang, Jorge M. Gonçalves, Yang Xiao, Maolin Wang, Yuqi Guo, Chuan Sun, Xiuchuan Tang, Liang Jing, Mingyang Zhang, Xiang Huang, Ying Xiao, Haosen Cao, Yanyan Chen, Tongxin Ren, Fang Wang, Yaru Xiao, Sufang Huang, Xi Tan, Niannian Huang et al. (10 additional authors not shown)

Abstract: Early and accurate clinical assessment of disease severity in COVID-19 patients is essential for planning the allocation of scarce hospital resources. An explainable machine learning tool trained on blood sample data from 485 patients from Wuhan selected three biomarkers for predicting mortality of individual patients with high accuracy.