The use of machine learning (ML) with electronic health records (EHR) is growing in
popularity as a means to extract knowledge that can improve the decision-making process in …

Artificial intelligence (AI) has entered the mainstream as computing power has improved.
The healthcare industry is undergoing dramatic transformations at present. One of the most …

While a growing number of machine learning (ML) systems have been deployed in clinical
settings with the promise of improving patient care, many have struggled to gain adoption …

Objective This work aims to provide a review of the existing literature in the field of
automated machine learning (AutoML) to help healthcare professionals better utilize …

Heart disease is one of the most significant causes of mortality in the world today. Prediction
of cardiovascular disease is a critical challenge in the area of clinical data analysis. Machine …

Big data in health care is a fast-growing field and a new paradigm that is transforming case-
based studies to large-scale, data-driven research. As big data is dependent on the …

Analysis of big data by machine learning offers considerable advantages for assimilation
and evaluation of large amounts of complex health-care data. However, to effectively use …

Machine Learning in Medicine In this view of the future of medicine, patient–provider
interactions are informed and supported by massive amounts of data from interactions with …

Health systems rely on commercial prediction algorithms to identify and help patients with
complex health needs. We show that a widely used algorithm, typical of this industry-wide …

Machine learning is used increasingly in clinical care to improve diagnosis, treatment
selection, and health system efficiency. Because machine-learning models learn from …