Machine learning of COVID-19 clinical data identifies population structures with therapeutic potential

Clinical outcomes for patients with COVID-19 are heterogeneous and there is interest in defining subgroups for prognostic modeling and development of treatment algorithms. We obtained 28 demographic and laboratory variables in patients admitted to hospital with COVID-19. These comprised a training c...

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Detalles Bibliográficos
Autores principales: Greenwood, David, Taverner, Thomas, Adderley, Nicola J., Price, Malcolm James, Gokhale, Krishna, Sainsbury, Christopher, Gallier, Suzy, Welch, Carly, Sapey, Elizabeth, Murray, Duncan, Fanning, Hilary, Ball, Simon, Nirantharakumar, Krishnarajah, Croft, Wayne, Moss, Paul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9153184/
https://www.ncbi.nlm.nih.gov/pubmed/35665240
http://dx.doi.org/10.1016/j.isci.2022.104480

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9153184/
https://www.ncbi.nlm.nih.gov/pubmed/35665240
http://dx.doi.org/10.1016/j.isci.2022.104480

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