Nilanjan Dey

Amira S. Ashour

Simon James Fong

Chintan Bhatt

Healthcare Data Analytics and Management help readers disseminate cutting-edge research that delivers insights into the analytic tools, opportunities, novel strategies, techniques and challenges for handling big data, data analytics and management in healthcare. As the rapidly expanding and heterogeneous nature of healthcare data poses challenges for big data analytics, this book targets researchers and bioengineers from areas of machine learning, data mining, data management, and healthcare providers, along with clinical researchers and physicians who are interested in the management and analysis of healthcare data. Covers data analysis, management and security concepts and tools in the healthcare domain Highlights electronic medical health records and patient information records Discusses the different techniques to integrate Big data and Internet-of-Things in healthcare, including machine learning and data mining Includes multidisciplinary contributions in relation to healthcare applications and challenges

Healthcare Data Analytics and Management

Richard C. Millham

This book aims to provide some insights into recently developed bio-inspired algorithms within recent emerging trends of fog computing, sentiment analysis, and data streaming as well as to provide a more comprehensive approach to the big data management from pre-processing to analytics to visualization phases. The subject area of this book is within the realm of computer science, notably algorithms (meta-heuristic and, more particularly, bio-inspired algorithms). Although application domains of these new algorithms may be mentioned, the scope of this book is not on the application of algorithms to specific or general domains but to provide an update on recent research trends for bio-inspired algorithms within a specific application domain or emerging area. These areas include data streaming, fog computing, and phases of big data management. One of the reasons for writing this book is that the bio-inspired approach does not receive much attention but shows considerable promise and diversity in terms of approach of many issues in big data and streaming. Some novel approaches of this book are the use of these algorithms to all phases of data management (not just a particular phase such as data mining or business intelligence as many books focus on); effective demonstration of the effectiveness of a selected algorithm within a chapter against comparative algorithms using the experimental method. Another novel approach is a brief overview and evaluation of traditional algorithms, both sequential and parallel, for use in data mining, in order to provide an overview of existing algorithms in use. This overview complements a further chapter on bio-inspired algorithms for data mining to enable readers to make a more suitable choice of algorithm for data mining within a particular context. In all chapters, references for further reading are provided, and in selected chapters, the author also include ideas for future research.

Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing

Wearable and Implantable Medical Devices: Applications and Challenges, Fourth Edition highlights the new aspects of wearable and implanted sensors technology in the healthcare sector and monitoring systems. The book's contributions include several interdisciplinary domains, such as wearable sensors, implanted sensors devices, Internet-of-Things (IoT), security, real-time medical healthcare monitoring, WIBSN design and data management, encryption, and decision-support systems. Contributions emphasize several topics, including real-world applications and the design and implementation of wearable devices. This book demonstrates that this new field has a brilliant future in applied healthcare research and in healthcare monitoring systems. Includes comprehensive information on wearable and implanted device technology, wearable and implanted sensors design, WIBSN requirements, WIBSN in monitoring systems and security concepts Highlights machine learning and computing in healthcare monitoring systems based on WIBSN Includes a multidisciplinary approach to different healthcare applications and their associated challenges based on wearable and implanted technologies

Simon James Fong

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