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...

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Detalles Bibliográficos
Autores principales: Kim, Sungmin, Yoon, Jaechul, Kym, Dohern, Hur, Jun, Kim, Myongjin, Park, Jongsoo, Cho, Yong Suk, Chun, Wook, Yoon, Dogeon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
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

Internet

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

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