Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients

Patients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic markers of disease progression and death. From a co...

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Detalles Bibliográficos
Autores principales: Jimenez-Solem, Espen, Petersen, Tonny S., Hansen, Casper, Hansen, Christian, Lioma, Christina, Igel, Christian, Boomsma, Wouter, Krause, Oswin, Lorenzen, Stephan, Selvan, Raghavendra, Petersen, Janne, Nyeland, Martin Erik, Ankarfeldt, Mikkel Zöllner, Virenfeldt, Gert Mehl, Winther-Jensen, Matilde, Linneberg, Allan, Ghazi, Mostafa Mehdipour, Detlefsen, Nicki, Lauritzen, Andreas David, Smith, Abraham George, de Bruijne, Marleen, Ibragimov, Bulat, Petersen, Jens, Lillholm, Martin, Middleton, Jon, Mogensen, Stine Hasling, Thorsen-Meyer, Hans-Christian, Perner, Anders, Helleberg, Marie, Kaas-Hansen, Benjamin Skov, Bonde, Mikkel, Bonde, Alexander, Pai, Akshay, Nielsen, Mads, Sillesen, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7864944/
https://www.ncbi.nlm.nih.gov/pubmed/33547335
http://dx.doi.org/10.1038/s41598-021-81844-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7864944/
https://www.ncbi.nlm.nih.gov/pubmed/33547335
http://dx.doi.org/10.1038/s41598-021-81844-x

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