Identifying acute illness phenotypes via deep temporal interpolation and clustering network on physiologic signatures
Abstract Using clustering analysis for early vital signs, unique patient phenotypes with distinct pathophysiological signatures and clinical outcomes may be revealed and support early clinical decision-making. Phenotyping using early vital signs has proven challenging, as vital signs are typically s...
Gorde:
| Egile Nagusiak: | , , , , , , , , , , |
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| Formatua: | Artigo |
| Hizkuntza: | Inglês |
| Argitaratua: |
Nature Portfolio
2024-04-01
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| Saila: | Scientific Reports |
| Sarrera elektronikoa: | https://doi.org/10.1038/s41598-024-59047-x |
| Etiketak: |
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