Data Analytics in Healthcare: A Tertiary Study

Abstract

The field of healthcare has seen a rapid increase in the applications of data analytics during the last decades. By utilizing different data analytic solutions, healthcare areas such as medical image analysis, disease recognition, outbreak monitoring, and clinical decision support have been automated to various degrees. Consequently, the intersection of healthcare and data analytics has received scientific attention to the point of numerous secondary studies. We analyze studies on healthcare data analytics, and provide a wide overview of the subject. This is a tertiary study, i.e., a systematic review of systematic reviews. We identified 45 systematic secondary studies on data analytics applications in different healthcare sectors, including diagnosis and disease profiling, diabetes, Alzheimer’s disease, and sepsis. Machine learning and data mining were the most widely used data analytics techniques in healthcare applications, with a rising trend in popularity. Healthcare data analytics studies often utilize four popular databases in their primary study search, typically select 25–100 primary studies, and the use of research guidelines such as PRISMA is growing. The results may help both data analytics and healthcare researchers towards relevant and timely literature reviews and systematic mappings, and consequently, towards respective empirical studies. In addition, the meta-analysis presents a high-level perspective on prominent data analytics applications in healthcare, indicating the most popular topics in the intersection of data analytics and healthcare, and provides a big picture on a topic that has seen dozens of secondary studies in the last 2 decades.

Effect weight training on muscular hypertrophy: a systematic review

“…To achieve proper progression, it is essential for individuals to regularly monitor their progress and record important figures such as the weight used, the number of reps, and training volume. In this way, an athlete or beginner in weight training can systematically increase their muscles' demands, stimulating more significant muscle growth [31].…”

Trivial State Fuzzy Processing for Error Reduction in Healthcare Big Data Analysis towards Precision Diagnosis

“…Additionally, a novel big data analytic technique using a fuzzy similarity measure model is employed in healthcare applications [ 15 ]. This analytic technique analyzes the potential data required to perform specific tasks in healthcare centers [ 16 ] and manages critical information collected from various divisions. The fuzzy-based technique reduces the data analysis inaccuracy ratio, enhancing the significance of healthcare applications [ 17 ].…”