Early risk assessment for COVID-19 patients from emergency department data using machine learning

Since its emergence in late 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic with more than 55 million reported cases and 1.3 million estimated deaths worldwide. While epidemiological and clinical characteristics of COVID-19 have been reported, risk factor...

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Veröffentlicht in:	Scientific reports Jg. 11; H. 1; S. 4200 - 13
Hauptverfasser:	Heldt, Frank S., Vizcaychipi, Marcela P., Peacock, Sophie, Cinelli, Mattia, McLachlan, Lachlan, Andreotti, Fernando, Jovanović, Stojan, Dürichen, Robert, Lipunova, Nadezda, Fletcher, Robert A., Hancock, Anne, McCarthy, Alex, Pointon, Richard A., Brown, Alexander, Eaton, James, Liddi, Roberto, Mackillop, Lucy, Tarassenko, Lionel, Khan, Rabia T.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 18.02.2021 Nature Publishing Group Nature Portfolio
Schlagworte:	631/114 631/114/1305 692/308 692/308/575 692/499 692/53/2422 692/699 692/699/255 692/699/255/2514 Adult Aged Aged, 80 and over Coronaviruses COVID-19 COVID-19 - mortality Creatinine Disease Progression Emergency medical care Emergency Service, Hospital - statistics & numerical data Epidemiology Female Hospital Mortality - trends Hospitalization - statistics & numerical data Hospitals - statistics & numerical data Humanities and Social Sciences Humans Intensive care Laboratories Lactic acid Learning algorithms London - epidemiology Machine Learning Male Mechanical ventilation Middle Aged Mortality Mortality risk multidisciplinary Oxygenation Pandemics Patients Respiration, Artificial - statistics & numerical data Retrospective Studies Risk assessment Risk Assessment - methods Risk Factors ROC Curve SARS-CoV-2 - isolation & purification Science Science (multidisciplinary) Severe acute respiratory syndrome coronavirus 2 United Kingdom - epidemiology Ventilation Ventilators London United Kingdom
ISSN:	2045-2322, 2045-2322
Online-Zugang:	Volltext
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