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Evaluating clinical heterogeneity and predicting mortality in severely burned patients through unsupervised clustering and latent class analysis
Burn injuries often result in a high level of clinical heterogeneity and poor prognosis in patients with severe burns. Clustering algorithms, which are unsupervised methods that can identify groups with similar trajectories in patients with heterogeneous diseases, can provide insights into the mecha...
| Autores principales: | , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10442401/ https://www.ncbi.nlm.nih.gov/pubmed/37604951 http://dx.doi.org/10.1038/s41598-023-40927-7 |