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...
Main Authors: | , , , , , , , , , |
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Format: | Article |
Language: | English |
Published: |
Nature Publishing Group
2021-06-01
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Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-021-91297-x |