tetano
Editor, Senior Moderator
Nat Commun
. 2020 Oct 6;11(1):5033.
doi: 10.1038/s41467-020-18684-2.
Machine learning based early warning system enables accurate mortality risk prediction for COVID-19
Yue Gao[SUP] 1 2 [/SUP], Guang-Yao Cai[SUP] 1 2 [/SUP], Wei Fang[SUP] 3 [/SUP], Hua-Yi Li[SUP] 1 2 [/SUP], Si-Yuan Wang[SUP] 1 2 [/SUP], Lingxi Chen[SUP] 4 [/SUP], Yang Yu[SUP] 1 2 [/SUP], Dan Liu[SUP] 1 2 [/SUP], Sen Xu[SUP] 1 2 [/SUP], Peng-Fei Cui[SUP] 1 2 [/SUP], Shao-Qing Zeng[SUP] 1 2 [/SUP], Xin-Xia Feng[SUP] 5 [/SUP], Rui-Di Yu[SUP] 1 2 [/SUP], Ya Wang[SUP] 1 2 [/SUP], Yuan Yuan[SUP] 1 2 [/SUP], Xiao-Fei Jiao[SUP] 1 2 [/SUP], Jian-Hua Chi[SUP] 1 2 [/SUP], Jia-Hao Liu[SUP] 1 2 [/SUP], Ru-Yuan Li[SUP] 1 2 [/SUP], Xu Zheng[SUP] 1 2 [/SUP], Chun-Yan Song[SUP] 1 2 [/SUP], Ning Jin[SUP] 1 2 [/SUP], Wen-Jian Gong[SUP] 1 2 [/SUP], Xing-Yu Liu[SUP] 1 2 [/SUP], Lei Huang[SUP] 6 [/SUP], Xun Tian[SUP] 6 [/SUP], Lin Li[SUP] 7 [/SUP], Hui Xing[SUP] 7 [/SUP], Ding Ma[SUP] 1 2 [/SUP], Chun-Rui Li[SUP] 8 [/SUP], Fei Ye[SUP] 9 [/SUP], Qing-Lei Gao[SUP] 10 11 [/SUP]
Affiliations
Abstract
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.
. 2020 Oct 6;11(1):5033.
doi: 10.1038/s41467-020-18684-2.
Machine learning based early warning system enables accurate mortality risk prediction for COVID-19
Yue Gao[SUP] 1 2 [/SUP], Guang-Yao Cai[SUP] 1 2 [/SUP], Wei Fang[SUP] 3 [/SUP], Hua-Yi Li[SUP] 1 2 [/SUP], Si-Yuan Wang[SUP] 1 2 [/SUP], Lingxi Chen[SUP] 4 [/SUP], Yang Yu[SUP] 1 2 [/SUP], Dan Liu[SUP] 1 2 [/SUP], Sen Xu[SUP] 1 2 [/SUP], Peng-Fei Cui[SUP] 1 2 [/SUP], Shao-Qing Zeng[SUP] 1 2 [/SUP], Xin-Xia Feng[SUP] 5 [/SUP], Rui-Di Yu[SUP] 1 2 [/SUP], Ya Wang[SUP] 1 2 [/SUP], Yuan Yuan[SUP] 1 2 [/SUP], Xiao-Fei Jiao[SUP] 1 2 [/SUP], Jian-Hua Chi[SUP] 1 2 [/SUP], Jia-Hao Liu[SUP] 1 2 [/SUP], Ru-Yuan Li[SUP] 1 2 [/SUP], Xu Zheng[SUP] 1 2 [/SUP], Chun-Yan Song[SUP] 1 2 [/SUP], Ning Jin[SUP] 1 2 [/SUP], Wen-Jian Gong[SUP] 1 2 [/SUP], Xing-Yu Liu[SUP] 1 2 [/SUP], Lei Huang[SUP] 6 [/SUP], Xun Tian[SUP] 6 [/SUP], Lin Li[SUP] 7 [/SUP], Hui Xing[SUP] 7 [/SUP], Ding Ma[SUP] 1 2 [/SUP], Chun-Rui Li[SUP] 8 [/SUP], Fei Ye[SUP] 9 [/SUP], Qing-Lei Gao[SUP] 10 11 [/SUP]
Affiliations
- PMID: 33024092
- DOI: 10.1038/s41467-020-18684-2
Abstract
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.