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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.
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.
The number of elderly is increasing in recent years. According to the United Nations (UN), in 2050 there will be 2.1 billion people above 60 years of age worldwide. In addition, the World Health Organization (WHO) reported that more than 1 billion people live with some form of disability, the leading causes of which are chronic diseases and accidents. Thus, many opportunities for the application of smart environments to support ubiquitous healthcare are emerging, the benefits of which may be reflected in reduced medical costs and increased convenience of patients and families. This systematic mapping study aims to identify how smart environments have been applied to support ubiquitous healthcare, what techniques and technologies are being used, and what research gaps are still left unexplored. Eight scientific repositories were used to search for papers in the area of ubiquitous healthcare, and a filtering process was used to remove bias. Of an initial sample of 1706 studies, 49 were reviewed entirely, analyzed, and categorized. Among these, we highlight those oriented to monitoring, detection, notification, and action on situations that may cause illnesses or promote the improvement of people’s health and wellness. Technologies to support ubiquitous healthcare were categorized into three groups: ambient sensors, wearables, and social robotics. These technologies have been applied most frequently to support the elderly and disabled. The diseases most commonly cited were dementia, diabetes, Alzheimer’s, autism, obesity, mental stress, sleep disorders, asthma, epilepsy and chronic diseases. We found only three papers that used prediction models. Finally, we observed a trend of using social robotics to improve the intelligence of ambient, aggregating mobility, and acting.
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