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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

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

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number5033
Journal / PublicationNature Communications
Volume11
Online published6 Oct 2020
Publication statusPublished - 2020

Link(s)

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.

Research Area(s)

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Citation Format(s)

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.

In: Nature Communications, Vol. 11, 5033, 2020.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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