Machine learning based early warning system enables accurate mortality risk prediction for COVID-19.
Nat Commun
; 11(1): 5033, 2020 10 06.
Article
in English
| MEDLINE | ID: covidwho-834877
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia, Viral
/
Coronavirus Infections
/
Pandemics
/
Machine Learning
Type of study:
Cohort study
/
Observational study
/
Prognostic study
Limits:
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Country/Region as subject:
Asia
Language:
English
Journal:
Nat Commun
Journal subject:
Biology
/
Science
Year:
2020
Document Type:
Article
Affiliation country:
S41467-020-18684-2
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