Risk prediction of clinical adverse outcomes with machine learning in a cohort of critically ill patients with atrial fibrillation
Abstract 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 ana...
Guardado en:
Autores principales: | Lorenzo Falsetti, Matteo Rucco, Marco Proietti, Giovanna Viticchi, Vincenzo Zaccone, Mattia Scarponi, Laura Giovenali, Gianluca Moroncini, Cinzia Nitti, Aldo Salvi |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/5eced6bb0775495d8144bc16703f310b |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Atrial Myopathy Underlying Atrial Fibrillation
por: Harold Rivner, et al.
Publicado: (2020) - Journal of atrial fibrillation
-
Metabolomic and Proteomic Analyses of Persistent Valvular Atrial Fibrillation and Non-Valvular Atrial Fibrillation
por: Bo Hu, et al.
Publicado: (2021) -
Atrial Fibrillation and Oral Health
por: Amaar Hassan, et al.
Publicado: (2021) -
PSYCHOSOMATIC CONNECTION IN ATRIAL FIBRILLATION
por: F. I. Belyalov
Publicado: (2014)