Until now, much of the work on machine learning and health has focused on processes
inside the hospital or clinic. However, this represents only a narrow set of tasks and …

The use of artificial intelligence, and the deep-learning subtype in particular, has been
enabled by the use of labeled big data, along with markedly enhanced computing power …

Artificial intelligence and machine learning are poised to influence nearly every aspect of the
human condition, and cardiology is not an exception to this trend. This paper provides a …

Deep learning has been applied to many areas in health care, including imaging diagnosis,
digital pathology, prediction of hospital admission, drug design, classification of cancer and …

Electronic medical records (EMRs) support the development of machine learning algorithms
for predicting disease incidence, patient response to treatment, and other healthcare events …

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 …

Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from
data, allowing computers to find hidden insights without being explicitly programmed where …

Cardiovascular disease (CVD) is the principal cause of mortality and morbidity globally. With
the pressures for improved care and translation of the latest medical advances and …

Chronic diseases are becoming more widespread. Treatment and monitoring of these
diseases require going to hospitals frequently, which increases the burdens of hospitals and …

Aims This study aimed to review the performance of machine learning (ML) methods
compared with conventional statistical models (CSMs) for predicting readmission and …