Abstract
Healthcare information systems can reduce the expenses of treatment, foresee episodes of pestilences, help stay away from preventable illnesses, and improve personal life satisfaction. As of late, considerable volumes of heterogeneous and differing medicinal services data are being produced from different sources covering clinic records of patients, lab results, and wearable devices, making it hard for conventional data processing to handle and manage this amount of data. Confronted with the difficulties and challenges facing the process of managing healthcare big data such as volume, velocity, and variety, healthcare information systems need to use new methods and techniques for managing and processing such data to extract useful information and knowledge. In the recent few years, a large number of organizations and companies have shown enthusiasm for using semantic web technologies with healthcare big data to convert data into knowledge and intelligence. In this paper, we review the state of the art on the semantic web for the healthcare industry. Based on our literature review, we will discuss how different techniques, standards, and points of view created by the semantic web community can participate in addressing the challenges related to healthcare big data.
Highlights
Big healthcare data refers to the process of collecting, integrating, managing, processing, and analyzing different kinds of medical data, which are excessively complex and inefficient to be processed and managed using existing database management systems and tools [1,2,3]
We study various ideas identified in managing healthcare big data and give broad insights concerning these ideas to assist readers with understanding the basic concepts introduced in this paper
Resource Description Framework (RDF) documents are written in XML and the language used by RDF is called RDF/ XML
Summary
Big healthcare data refers to the process of collecting, integrating, managing, processing, and analyzing different kinds of medical data, which are excessively complex and inefficient to be processed and managed using existing database management systems and tools [1,2,3]. Is data is generated from different resources such as clinical records, hospitals, patient monitoring devices, medical images, and lab results. Medical image management remains an energizing field of exploration and applications for healthcare and biomedical research. E following are a few applications which list some of the motivations for using semantic techniques to allocate big data in the healthcare domain [13]:.
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