Machine learning based early warning system enables accurate mortality risk prediction for COVID-19.
Nat Commun
; 11(1): 5033, 2020 10 06.
Article
em En
| MEDLINE
| ID: mdl-33024092
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.
Texto completo:
1
Coleções:
01-internacional
Contexto em Saúde:
2_ODS3
/
4_TD
/
6_ODS3_enfermedades_notrasmisibles
Base de dados:
MEDLINE
Assunto principal:
Pneumonia Viral
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Infecções por Coronavirus
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Pandemias
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Aprendizado de Máquina
Tipo de estudo:
Etiology_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Aged
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Female
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Humans
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Male
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Middle aged
País/Região como assunto:
Asia
Idioma:
En
Revista:
Nat Commun
Ano de publicação:
2020
Tipo de documento:
Article