Cargando…
Machine Learning Algorithms for Prediction of Survival by Stress Echocardiography in Chronic Coronary Syndromes
Stress echocardiography (SE) is based on regional wall motion abnormalities and coronary flow velocity reserve (CFVR). Their independent prognostic capabilities could be better studied with a machine learning (ML) approach. The study aims to assess the SE outcome data by conducting an analysis with...
Autores principales: | Cortigiani, Lauro, Azzolina, Danila, Ciampi, Quirino, Lorenzoni, Giulia, Gaibazzi, Nicola, Rigo, Fausto, Gherardi, Sonia, Bovenzi, Francesco, Gregori, Dario, Picano, Eugenio |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9504503/ https://www.ncbi.nlm.nih.gov/pubmed/36143307 http://dx.doi.org/10.3390/jpm12091523 |
Ejemplares similares
-
Dual imaging stress echocardiography versus computed tomography coronary angiography for risk stratification of patients with chest pain of unknown origin
por: Ciampi, Quirino, et al.
Publicado: (2015) -
The impact of aging and atherosclerotic risk factors on transthoracic coronary flow reserve in subjects with normal coronary angiography
por: Galderisi, Maurizio, et al.
Publicado: (2012) -
Coronary Flow, Left Ventricular Contractile and Heart Rate Reserve in Non-Ischemic Heart Failure
por: Daros, Clarissa Borguezan, et al.
Publicado: (2021) -
Prognostic value of stress echocardiography assessed by the ABCDE protocol
por: Ciampi, Quirino, et al.
Publicado: (2021) -
Assessing functional mitral regurgitation with exercise echocardiography: rationale and clinical applications
por: Bigi, Riccardo, et al.
Publicado: (2009)