Cargando…
Parsimonious machine learning models to predict resource use in cardiac surgery across a statewide collaborative
OBJECTIVE: We sought to several develop parsimonious machine learning models to predict resource utilization and clinical outcomes following cardiac operations using only preoperative factors. METHODS: All patients undergoing coronary artery bypass grafting and/or valve operations were identified in...
Autores principales: | Verma, Arjun, Sanaiha, Yas, Hadaya, Joseph, Maltagliati, Anthony Jason, Tran, Zachary, Ramezani, Ramin, Shemin, Richard J., Benharash, Peyman |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9510828/ https://www.ncbi.nlm.nih.gov/pubmed/36172420 http://dx.doi.org/10.1016/j.xjon.2022.04.017 |
Ejemplares similares
-
Defining value in cardiac surgery: A contemporary analysis of cost variation across the United States
por: Hadaya, Joseph, et al.
Publicado: (2022) -
Staged versus concomitant transcatheter aortic valve replacement and percutaneous coronary intervention: A national analysis
por: Tran, Zachary, et al.
Publicado: (2022) -
Influence of center surgical aortic valve volume on outcomes of transcatheter aortic valve replacement
por: Gandjian, Matthew, et al.
Publicado: (2022) -
Machine learning-based modeling of acute respiratory failure following emergency general surgery operations
por: Hadaya, Joseph, et al.
Publicado: (2022) -
Implementation and outcomes of an urban mobile adult extracorporeal life support program
por: Hadaya, Joseph, et al.
Publicado: (2022)