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

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’...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7538910/
https://www.ncbi.nlm.nih.gov/pubmed/33024092
http://dx.doi.org/10.1038/s41467-020-18684-2
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author 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
author_facet 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
author_sort Gao, Yue
collection PubMed
description 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.
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spelling pubmed-75389102020-10-19 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 Nat Commun Article 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. Nature Publishing Group UK 2020-10-06 /pmc/articles/PMC7538910/ /pubmed/33024092 http://dx.doi.org/10.1038/s41467-020-18684-2 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
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
Machine learning based early warning system enables accurate mortality risk prediction for COVID-19
title Machine learning based early warning system enables accurate mortality risk prediction for COVID-19
title_full Machine learning based early warning system enables accurate mortality risk prediction for COVID-19
title_fullStr Machine learning based early warning system enables accurate mortality risk prediction for COVID-19
title_full_unstemmed Machine learning based early warning system enables accurate mortality risk prediction for COVID-19
title_short Machine learning based early warning system enables accurate mortality risk prediction for COVID-19
title_sort machine learning based early warning system enables accurate mortality risk prediction for covid-19
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7538910/
https://www.ncbi.nlm.nih.gov/pubmed/33024092
http://dx.doi.org/10.1038/s41467-020-18684-2
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