单位：[1]National Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China华中科技大学同济医学院附属同济医院保健科（公共卫生科）党政职能科室国家重大公共卫生事件医学中心[2]Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China华中科技大学同济医学院附属同济医院妇产科学系普通妇科[3]GNSS Research Center, Wuhan University, Wuhan 430079, China[4]City University of Hong Kong Shenzhen Research Institute, Shenzhen 518000, China[5]Department of Gastroenterology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China华中科技大学同济医学院附属同济医院消化内科[6]Department of Obstetrics and Gynecology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China[7]Department of Obstetrics and Gynecology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China[8]Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China华中科技大学同济医学院附属同济医院血液内科[9]Department of Neurosurgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China华中科技大学同济医学院附属同济医院外科学系神经外科
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.
基金：
This
study was funded by the National Science and Technology Major Sub-Project
(2018ZX10301402-002), the Technical Innovation Special Project of Hubei Province
(2018ACA138), the National Natural Science Foundation of China (81772787, 81873452,
and 81974405), the Fundamental Research Funds for the Central Universities
(2019kfyXMBZ024), and Nature Science Foundation of Hubei Province (2019CFB453).
第一作者单位：[1]National Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China[2]Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China
共同第一作者：
通讯作者：
通讯机构：[1]National Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China[2]Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China[9]Department of Neurosurgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China
推荐引用方式(GB/T 7714)：
Gao Yue,Cai Guang-Yao,Fang Wei,et al.Machine learning based early warning system enables accurate mortality risk prediction for COVID-19.[J].NATURE COMMUNICATIONS.2020,11(1):5033.doi:10.1038/s41467-020-18684-2.
APA：
Gao Yue,Cai Guang-Yao,Fang Wei,Li Hua-Yi,Wang Si-Yuan...&Gao Qing-Lei.(2020).Machine learning based early warning system enables accurate mortality risk prediction for COVID-19..NATURE COMMUNICATIONS,11,(1)
MLA：
Gao Yue,et al."Machine learning based early warning system enables accurate mortality risk prediction for COVID-19.".NATURE COMMUNICATIONS 11..1(2020):5033
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