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

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

Abstract 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 provid...

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Autores principales: José Castela Forte, Galiya Yeshmagambetova, Maureen L. van der Grinten, Bart Hiemstra, Thomas Kaufmann, Ruben J. Eck, Frederik Keus, Anne H. Epema, Marco A. Wiering, Iwan C. C. van der Horst
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
Medicine
R
Science
Q
Acceso en línea:https://doaj.org/article/7e1247669dad4ed396c906d967f51c73
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