A wealth of genetic, demographic, clinical and biomarker data is collected from routine
clinical care of HIV patients and exists in the form of medical records available among the …

Machine learning (ML) has emerged as a transformative technology in healthcare,
revolutionizing disease diagnosis and treatment paradigms. This research explores the …

In the last few years there is a remarkable progress of research in machine learning. This
field has gained an unprecedented popularity, several new areas have developed and …

Futurists have anticipated that novel autonomous technologies, embedded with machine
learning (ML), will substantially influence healthcare. ML is focused on making predictions …

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 …

Drug discovery and manufacturing are adequately time-consuming, complicated, and costly
processes that depend on several parameters. Machine learning (ML) is becoming …

Background The extreme flexibility of the HIV type-1 (HIV-1) genome makes it challenging to
build the ideal antiretroviral treatment regimen. Interpretation of HIV-1 genotypic drug …

Due to the abundance of health data and growing computational power, machine learning
(ML) is engaging health researchers in a process of discovery around developing data …

The increasing availability of electronic health data presents a major opportunity in
healthcare for both discovery and practical applications to improve healthcare. However, for …

BackgroundThis paper aims to synthesise the literature on machine learning (ML) and big
data applications for mental health, highlighting current research and applications in …