Cargando…
A Machine Learning Model to Predict Cardiovascular Events during Exercise Evaluation in Patients with Coronary Heart Disease
Objective: To develop and optimize a machine learning prediction model for cardiovascular events during exercise evaluation in patients with coronary heart disease (CHD). Methods: 16,645 cases of cardiopulmonary exercise testing (CPET) conducted in patients with CHD from January 2016 to September 20...
Autores principales: | Shen, Tao, Liu, Dan, Lin, Zi, Ren, Chuan, Zhao, Wei, Gao, Wei |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9605581/ https://www.ncbi.nlm.nih.gov/pubmed/36294382 http://dx.doi.org/10.3390/jcm11206061 |
Ejemplares similares
-
Development and Validation of a Prediction Model for Cardiovascular Events in Exercise Assessment of Coronary Heart Disease Patients After Percutaneous Coronary Intervention
por: Shen, Tao, et al.
Publicado: (2022) -
Comparison Between Treadmill and Bicycle Ergometer Exercises in Terms of Safety of Cardiopulmonary Exercise Testing in Patients With Coronary Heart Disease
por: Ren, Chuan, et al.
Publicado: (2022) -
Effect of Smartphone-Based Telemonitored Exercise Rehabilitation among Patients with Coronary Heart Disease
por: Song, Yanxin, et al.
Publicado: (2019) -
Risk Prediction of Major Adverse Cardiovascular Events Occurrence Within 6 Months After Coronary Revascularization: Machine Learning Study
por: Wang, Jinwan, et al.
Publicado: (2022) -
Prediction of incident cardiovascular events using machine learning and CMR radiomics
por: Pujadas, Esmeralda Ruiz, et al.
Publicado: (2022)