Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia—Challenges, strengths, and opportunities in a global health emergency

AIMS: The aim of this study was to estimate a 48 hour prediction of moderate to severe respiratory failure, requiring mechanical ventilation, in hospitalized patients with COVID-19 pneumonia. METHODS: This was an observational prospective study that comprised consecutive patients with COVID-19 pneum...

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Detalles Bibliográficos
Autores principales: Ferrari, Davide, Milic, Jovana, Tonelli, Roberto, Ghinelli, Francesco, Meschiari, Marianna, Volpi, Sara, Faltoni, Matteo, Franceschi, Giacomo, Iadisernia, Vittorio, Yaacoub, Dina, Ciusa, Giacomo, Bacca, Erica, Rogati, Carlotta, Tutone, Marco, Burastero, Giulia, Raimondi, Alessandro, Menozzi, Marianna, Franceschini, Erica, Cuomo, Gianluca, Corradi, Luca, Orlando, Gabriella, Santoro, Antonella, Digaetano, Margherita, Puzzolante, Cinzia, Carli, Federica, Borghi, Vanni, Bedini, Andrea, Fantini, Riccardo, Tabbì, Luca, Castaniere, Ivana, Busani, Stefano, Clini, Enrico, Girardis, Massimo, Sarti, Mario, Cossarizza, Andrea, Mussini, Cristina, Mandreoli, Federica, Missier, Paolo, Guaraldi, Giovanni
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7660476/
https://www.ncbi.nlm.nih.gov/pubmed/33180787
http://dx.doi.org/10.1371/journal.pone.0239172

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7660476/
https://www.ncbi.nlm.nih.gov/pubmed/33180787
http://dx.doi.org/10.1371/journal.pone.0239172

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