Globally, there is a substantial unmet need to diagnose various diseases effectively. The
complexity of the different disease mechanisms and underlying symptoms of the patient …

Data science is likely to lead to major changes in cardiovascular imaging. Problems with
timing, efficiency, and missed diagnoses occur at all stages of the imaging chain. The …

Background The coronavirus disease (COVID-19) hospitalized patients are always at risk of
death. Machine learning (ML) algorithms can be used as a potential solution for predicting …

Omics techniques generate large, multidimensional data that are amenable to analysis by
new informatics approaches alongside conventional statistical methods. Systems theories …

Background This study sought to evaluate the performance of machine learning (ML)
models and establish an explainable ML model with good prediction of 3-year all-cause …

Artificial intelligence (AI) is a field of computer science that aims to mimic human thought
processes, learning capacity, and knowledge storage. AI techniques have been applied in …

Artificial intelligence (AI) has transformed key aspects of human life. Machine learning (ML),
which is a subset of AI wherein machines autonomously acquire information by extracting …

Introduction. Coronary artery disease (CAD) is one of the main causes of death all over the
world. One way to reduce the mortality rate from CAD is to predict its risk and take effective …

As radiology is inherently a data-driven specialty, it is especially conducive to utilizing data
processing techniques. One such technique, deep learning (DL), has become a remarkably …

The electrocardiogram (ECG) is a widely used medical test, consisting of voltage versus time
traces collected from surface recordings over the heart. Here we hypothesized that a deep …