Predicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumonia

Predicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumonia

Abstract In a pandemic with a novel disease, disease-specific prognosis models are available only with a delay. To bridge the critical early phase, models built for similar diseases might be applied. To test the accuracy of such a knowledge transfer, we investigated how precise lethal courses in cri...

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Autores principales: Gregor Lichtner, Felix Balzer, Stefan Haufe, Niklas Giesa, Fridtjof Schiefenhövel, Malte Schmieding, Carlo Jurth, Wolfgang Kopp, Altuna Akalin, Stefan J. Schaller, Steffen Weber-Carstens, Claudia Spies, Falk von Dincklage
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
Medicine
R
Science
Q
Acceso en línea:https://doaj.org/article/9f0925fd5b874cefbef161561d71441b
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https://doaj.org/article/9f0925fd5b874cefbef161561d71441b

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