Using Unsupervised Machine Learning to Identify Age- and Sex-Independent Severity Subgroups Among Patients with COVID-19: Observational Longitudinal Study

BACKGROUND: Early detection and intervention are the key factors for improving outcomes in patients with COVID-19. OBJECTIVE: The objective of this observational longitudinal study was to identify nonoverlapping severity subgroups (ie, clusters) among patients with COVID-19, based exclusively on cli...

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Detalles Bibliográficos
Autores principales: Benito-León, Julián, del Castillo, Mª Dolores, Estirado, Alberto, Ghosh, Ritwik, Dubey, Souvik, Serrano, J Ignacio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2021
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8163491/
https://www.ncbi.nlm.nih.gov/pubmed/33872186
http://dx.doi.org/10.2196/25988

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8163491/
https://www.ncbi.nlm.nih.gov/pubmed/33872186
http://dx.doi.org/10.2196/25988

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