Abstract Machine learning is as growing as fast as concepts such as Big data and the field of
data science in general. The purpose of the systematic review was to analyze scholarly …

Network pharmacology is an emerging area of systematic drug research that attempts to
understand drug actions and interactions with multiple targets. Network pharmacology has …

Although decision‐making algorithms are not new to medicine, the availability of vast stores
of medical data, gains in computing power, and breakthroughs in machine learning are …

Supervised and unsupervised learning algorithms have shown a great potential in
knowledge acquisition from large data sets. Supervised learning reflects the ability of an …

Background Machine learning (ML) is a growing field in medicine. This narrative review
describes the current body of literature on ML for clinical decision support in infectious …

Advances in algorithmic fairness have largely omitted sexual orientation and gender identity.
We explore queer concerns in privacy, censorship, language, online safety, health, and …

Alzheimer's disease (AD) remains a devastating neurodegenerative disease with few
preventive or curative treatments available. Modern technology developments of high …

Background Machine learning (ML) is increasingly being used in many areas of health care.
Its use in infection management is catching up as identified in a recent review in this journal …

COVID-19 is one of the greatest challenges humanity has faced recently, forcing a change in
the daily lives of billions of people worldwide. Therefore, many efforts have been made by …

Rapid advances in artificial intelligence (AI) and availability of biological, medical, and
healthcare data have enabled the development of a wide variety of models. Significant …