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: | , , , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/7e1247669dad4ed396c906d967f51c73 |
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