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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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| Veröffentlicht in: | Sci Rep |
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| Hauptverfasser: | , , , , , , , , , |
| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
Nature Publishing Group UK
2021
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| Schlagworte: | |
| Online Zugang: | 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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