Big Data Analytics for Intelligent Healthcare Management (Advances in ubiquitous sensing applications for healthcare)

The biggest technological challenge in Big Data is to provide a mechanism for storage, manipulation, and retrieval of information on large amounts of data. In this context, the healthcare industry is also being challenged with the difficulties of capturing data, storing data, analysis of data and data visualization. Due to the rapid growth of large volume of information generated on a daily basis, the use of existing healthcare infrastructure has become impracticable to handle this issue. So, it is essential to develop better intelligent techniques, skills and tools to deal with the patient data and its inherent insights automatically. Intelligent healthcare management technologies can play an effective role to tackle this challenge and change the future for improving our lives. Therefore, there is increasing interest in exploring and unlocking the value of the massively available data within the healthcare domain. Healthcare organizations also need to continuously discover useful and actionable knowledge and gain insight from raw data for various purposes such as saving lives, reducing medical errors, increasing efficiency, reducing costs and improving patient outcomes dynamically. Data analytics in intelligent healthcare management brings great challenge and also playing an important role in intelligent healthcare management system. Big Data Analytics for Intelligent Healthcare Management covers both the theory and application of hardware platforms and architectures, development of software methods, techniques and tools, applications and governance and adoption strategies for the use of big data in healthcare and clinical research. The book provides the latest research findings on the use of big data analytics with statistical and machine learning techniques to analyze huge amounts of real-time healthcare data. High dimensional data with multi-objective problems in healthcare is the primary open issue in big data, and this issue is covered extensively by the editors of this book. Heterogeneous healthcare data in various forms such as text, images, and video, and other detailed clinical data are required to be effectively stored, processed, and analyzed to avoid the increasing cost of health care and medical errors. Big Data Analytics for Intelligent Healthcare Management provides readers with insights into the design of intelligent healthcare systems to manage the rapid growth of high-dimensional real-time clinical data in an efficient way. Examines the methodology and requirements for development of big data architecture, big data modeling, big data as a service, big data analytics, big data for business modeling, big data privacy in healthcare, as well as decision and risk analysis Discusses big data applications for intelligent healthcare management such as revenue management and pricing, predictive analytics/forecasting, big data integration for medical data, algorithms and techniques to speed up the analysis of big medical data, business Intelligence for medical and healthcare data, disease diagnostic predictive models, data models and architectures for healthcare, healthcare data integration Covers development of big data tools such as data, web and text mining, data mining, optimization, machine learning, cloud in big data with Hadoop, big data in IoT, data analytics with machine learning tools, data analytics with optimization techniques, data analytics in enterprise applications, social network analysis in healthcare, big data analytics for smart cities, and data analytics for clinical applications

• | Author: Nilanjan Dey, Himansu Das, Bighnaraj Naik, H S Behera
• | Publisher: Academic Press
• | Publication Date: Apr 13, 2019
• | Number of Pages: 312 pages
• | Language: English
• | Binding: Paperback
• | ISBN-10: 012818146X
• | ISBN-13: 9780128181461