Abstract Machine Learning (ML) studies algorithms which can learn from data to gain
knowledge from experience and to make decisions and predictions. Health Informatics (HI) …

Healthcare informatics, a multi-disciplinary field has become synonymous with the
technological advancements and big data challenges. With the need to reduce healthcare …

Abstract Machine learning (ML) is the fastest growing field in computer science, and health
informatics is among the greatest challenges. The goal of ML is to develop algorithms which …

There is scarcely any business that does not profit from increased IT resources and self-
learning computer networks–but in the healthcare sector, AI may be employed in a variety of …

Modern electronic health records (EHRs) provide data to answer clinically meaningful
questions. The growing data in EHRs makes healthcare ripe for the use of machine learning …

Applications of machine learning (ML) methods have been used extensively to solve various
complex challenges in recent years in various application areas, such as medical, financial …

Objectives: To review how health informatics systems based on machine learning methods
have impacted the clinical management of patients, by affecting clinical practice. Methods …

The explosive growth and widespread accessibility of digital health data have led to a surge
of research activity in the healthcare and data sciences fields. The conventional approaches …

Rapid recent advances in automated data collection routines in clinical sciences have led to
a tsunami of patient-oriented data stored in distributed, heterogeneous, and large databases …

Abstract Machine learning as an advanced computational technology has been around for
several years in discovering patterns from diverse biomedical data sources and providing …