Risk prediction of clinical adverse outcomes with machine learning in a cohort of critically ill patients with atrial fibrillation

Critically ill patients affected by atrial fibrillation are at high risk of adverse events: however, the actual risk stratification models for haemorrhagic and thrombotic events are not validated in a critical care setting. With this paper we aimed to identify, adopting topological data analysis, th...

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Detalles Bibliográficos
Autores principales: Falsetti, Lorenzo, Rucco, Matteo, Proietti, Marco, Viticchi, Giovanna, Zaccone, Vincenzo, Scarponi, Mattia, Giovenali, Laura, Moroncini, Gianluca, Nitti, Cinzia, Salvi, Aldo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8460701/
https://www.ncbi.nlm.nih.gov/pubmed/34556682
http://dx.doi.org/10.1038/s41598-021-97218-2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8460701/
https://www.ncbi.nlm.nih.gov/pubmed/34556682
http://dx.doi.org/10.1038/s41598-021-97218-2

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