The role of physicians has always been to synthesize the data available to them to identify
diagnostic patterns that guide treatment and follow response. Today, increasingly …

In the recent decade, deep learning, a subset of artificial intelligence and machine learning,
has been used to identify patterns in big healthcare datasets for disease phenotyping, event …

Atrial fibrillation is a heart arrhythmia strongly associated with other heart-related
complications that can increase the risk of strokes and heart failure. Manual …

Background The electrocardiogram (ECG) is one of the most commonly used diagnostic
tools in medicine and healthcare. Deep learning methods have achieved promising results …

Compared with common deep learning methods (eg, convolutional neural networks),
transfer learning is characterized by simplicity, efficiency and its low training cost, breaking …

With the fast progress in information technologies and artificial intelligence (AI), smart
healthcare has gained considerable momentum. By using advanced technologies like AI …

Myocardial infarction is a common cardiovascular disorder caused by prolonged ischemia,
and early diagnosis of myocardial infarction (MI) is critical for lifesaving. ECG is a simple and …

Background Transfer learning is a form of machine learning where a pre-trained model
trained on a specific task is reused as a starting point and tailored to another task in a …

Abstract Background and Motivation: Cardiovascular disease (CVD) causes the highest
mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment …

With the emergence of the Internet of Things (IoT), investigation of different diseases in
healthcare improved, and cloud computing helped to centralize the data and to access …