Healthcare Data Analysis in the Internet of Things Era

Healthcare Data Analysis in the Internet of Things Era

George Tzanis (Aristotle University of Thessaloniki, Greece)
Copyright: © 2018 |Pages: 11
DOI: 10.4018/978-1-5225-2255-3.ch172
OnDemand PDF Download:
List Price: $37.50


Undoubtedly the IoT is the future of technology, which can provide manifold benefits to health care. However, the challenges posed are also great. Concerning the analysis of healthcare data, various tools have been introduced to deal efficiently with the large volumes as well as the various peculiarities of data (e.g. missing values, noise, etc.). Most popular representative of these modern tools is data mining, or the KDD process, strictly speaking. Although the KDD process has provided a lot of solutions, in many cases these techniques have to be scaled in order to deal with the new challenges posed by the big data paradigm. Cloud computing is the modern infrastructure that can provide the means to efficiently manage big data. Both cloud computing and the IoT are very promising concepts of technology and their complementary characteristics assure that their integration, Cloud-IoT, is very promising too. The introduction of the Cloud-IoT paradigm in the healthcare domain can offer manifold benefits and opportunities that will considerably improve the quality of health care.
Chapter Preview


Health care advances are taking place rapidly all over the world. The newest medical discoveries and higher quality of medical practice have guided to serious improvements in human health and well-being. Life expectancy has been considerably increasing, leading to an aging population with different needs and desires. Moreover, world population has been rising exponentially, challenging the effectiveness of healthcare systems. New tests, procedures, and medicines have made medical care increasingly expensive, forcing the restraint of costs even while overall effectiveness improves (Vasanth & Sbert, 2012). Nowadays, prevention and early detection of disease signs as well as quality of life are becoming the points of focus more than ever before. Furthermore, personalized medicine is deemed particularly important and promises better disease treatment for each individual. Patients are becoming better-informed, want to know more about their health and their treatment, and demand to be involved in decision-making regarding their health.

All the aforementioned trends and changes have revealed the necessity to adopt a different model of health care. The modern healthcare delivery system should be more patient-centric, rather than career-centric, in order to deal effectively with the new challenges. Such a healthcare system should be organized in a layered structure (Pang, 2013) as shown in Figure 1. For example, the lower layer should comprise the single person and the higher one the hospital. Between these two terminal layers one can find the home and community layers. The lowest layer (person) is characterized by the lowest labor intensity and operational cost, as well as the highest frequency of usage for chronic disease and lowest frequency of usage for acute disease. In contrast, the highest layer (hospital) has the highest labor intensity and operational cost, as well as the highest frequency of usage for acute disease and lowest for chronic disease.

The delivery model of health care is now standing at the starting point of its way to transform from the traditional hospital-centric to the modern home-centric/person-centric approach. The Internet of Things with its pervasive, personalized, and ubiquitous character can guide health care through this way. Many relevant concepts have been introduced to describe the future healthcare model powered by emerging information and communication technologies. Various terms, such as connected health, health Internet of Things (Health-IoT), pervasive healthcare (pHelath), ubiquitous healthcare (uHealth), mobile healthcare (mHealth), electrical healthcare (eHealth), telehealth, telemedicine, etc., have been utilized in order to express the plethora of these new concepts (Pang, 2013; Pawar et al., 2012; Rose et al., 2015). All these new concepts include, more or less, the fundamental aspects of the home-centric/person-centric healthcare model.

This article presents the concept of the Internet of Things as applied in health care discussing the benefits and opportunities as well as the posed obstacles and challenges. The article focuses on the basic strategies for analyzing healthcare data generated by the Internet of Things enabled devices and describes the current status, the major challenges, and the future trends of this revolutionary field.

Figure 1.

The layered structure of a modern healthcare system



According to the definition provided by the telecommunication standardization sector of the International Telecommunication Union (ITU-T Recommendations, 2012), the Internet of Things (IoT) is “a global infrastructure for the information society, enabling advanced services by interconnecting (physical and virtual) things based on existing and evolving interoperable information and communication technologies”. With regard to the IoT, the term “thing” is defined as “an object of the physical world (physical thing) or the information world (virtual thing), which is capable of being identified and integrated into communication networks”. Exploiting various capabilities, concerning identification, data capture, processing, communication, etc., the IoT uses any “thing” to offer any service to any application domain. At the same time IoT has to abide by the requirements for security and privacy (ITU-T Recommendations, 2012).

Key Terms in this Chapter

Wi-Fi: The technology that allows electronic devices to connect to a wireless local area network (WLAN) and Internet using radio waves.

Ambient Intelligence: The discipline aims to bring intelligence to everyday environments making them sensitive and responsive to the presence of people in a non-intrusive way and with minimal explicit interaction.

Artificial Intelligence: The area of computer science that studies the construction of computer programs that can imitate human cognitive abilities and present behaviors indistinguishable from human behavior.

Wireless Sensor Network (WSN): The network of spatially distributed autonomous sensors which monitor physical or environmental conditions and cooperatively pass their data through the network to a main location.

Radio Frequency Identification (RFID): The technology that incorporates the use of electromagnetic fields to uniquely identify and track a tagged object.

Machine Learning: The field of study that deals with the construction of algorithms that can learn from data and make predictions on data.

Ubiquitous Computing: The discipline aims to bring computing anywhere and anytime in any object. It is also called pervasive computing.

Internet of Medical Things (IoMT): A global infrastructure consisting of the collection of medical devices and applications that are interconnected through information and communication technologies.

Complete Chapter List

Search this Book: