Handbook of Data Science Approaches for Biomedical Engineering

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Handbook of Data Science Approaches for Biomedical Engineering is written by Valentina E Balas and published by Academic Press. It's available with International Standard Book Number or ISBN identification 0128183187 (ISBN 10) and 9780128183182 (ISBN 13).

Handbook of Data Science Approaches for Biomedical Engineering covers the research issues and concepts of biomedical engineering progress in alignment with the latest technologies of Big Data and the Internet of Things. The book includes the most current research developments in the field of biomedical engineering applications based on IoT and Big data, including various real-time and offline medical applications that directly or indirectly rely on medical and information technology. The book also includes case studies in the field of medical science, biomedical engineering, computer science, information security, interdisciplinary tools, along with the latest tools and technologies used. Ninety percent of data currently available in the world has been generated in past couple of years, and the volume of data is rapidly increasing day by day. The reason for this growth is the increase in communication via electronic devices, sensors, web logs, global positioning system (GPS) data, mobile data, and other aspects of the Internet of Things. This data may come in a continuous flow, may be from various sources, and likely impacts technical systems in real time, which may lead to a problem of managing and processing data using traditional methods. This wide variety of data flows, sources, and types may also overwhelm your systems. Handbook of Data Science Approaches for Biomedical Engineering showcases the most cutting-edge information management tools and technologies to deal with the large volume of real-time data. Provides in-depth information about Biomedical Engineering with Big Data and Internet of Things Includes technical approaches for solving real-time healthcare problems and practical solutions through case studies in big data and Internet of Things Discusses big data applications for healthcare management such as predictive analytics andforecasting, big data integration for medical data, algorithms and techniques to speed up the analysis of big medical data, big data in IoT, and data analytics with machine learning tools