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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Опубликовано в: :Sci Rep
Главные авторы: 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.
Формат: Artigo
Язык:Inglês
Опубликовано: Nature Publishing Group UK 2021
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Article
Online-ссылка:https://ncbi.nlm.nih.gov/pmc/articles/PMC8187398/
https://ncbi.nlm.nih.gov/pubmed/34103544
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41598-021-91297-x
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