Identifying and characterizing high-risk clusters in a heterogeneous ICU population with deep embedded clustering

Critically ill patients constitute a highly heterogeneous population, with seemingly distinct patients having similar outcomes, and patients with the same admission diagnosis having opposite clinical trajectories. We aimed to develop a machine learning methodology that identifies and provides better...

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Detalles Bibliográficos
Autores principales: Castela Forte, José, Yeshmagambetova, Galiya, van der Grinten, Maureen L., Hiemstra, Bart, Kaufmann, Thomas, Eck, Ruben J., Keus, Frederik, Epema, Anne H., Wiering, Marco A., van der Horst, Iwan C. C.
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/PMC8187398/
https://www.ncbi.nlm.nih.gov/pubmed/34103544
http://dx.doi.org/10.1038/s41598-021-91297-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8187398/
https://www.ncbi.nlm.nih.gov/pubmed/34103544
http://dx.doi.org/10.1038/s41598-021-91297-x

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