Comparison of Electrocardiographic Parameters by Gender in Heart Failure Patients with Preserved Ejection Fraction via Artificial Intelligence.

Published in: Diagnostics (Basel, Switzerland) (2023)

The proposed model serves as a valuable tool for physicians, facilitating the diagnosis, treatment, and follow-up for distinguishing male patients with HFpEF from female patients with HFpEF. Analyzing readily accessible electrocardiographic parameters empowers medical professionals to make informed decisions and provide enhanced care to a wide range of individuals.

Keyphrases

ejection fraction
artificial intelligence
aortic stenosis
machine learning
healthcare
big data
deep learning
left ventricular
primary care
palliative care
quality improvement
combination therapy
pain management
coronary artery disease
mitral valve
transcatheter aortic valve replacement
replacement therapy