Medical Big Data and Internet of Medical Things : Advances, Challenges and Applications

Big data and the Internet of Things (IoT) play a vital role in prediction systems used in biological and medical applications, particularly for resolving issues related to disease biology at different scales. Modelling and integrating medical big data with the IoT helps in building effective prediction systems for automatic recommendations of diagnosis and treatment. The ability to mine, process, analyse, characterize, classify and cluster a variety and wide volume of medical data is a challenging task. There is a great demand for the design and development of methods dealing with capturing and automatically analysing medical data from imaging systems and IoT sensors. Addressing analytical and legal issues, and research on integration of big data analytics with respect to clinical practice and clinical utility, architectures and clustering techniques for IoT data processing, effective frameworks for removal of misclassified instances, practicality of big data analytics, methodological and technical issues, potential of Hadoop in managing healthcare data is the need of the hour. This book integrates different aspects used in the field of healthcare such as big data, IoT, soft computing, machine learning, augmented reality, organs on chip, personalized drugs, implantable electronics, integration of bio-interfaces, and wearable sensors, devices, practical body area network (BAN) and architectures of web systems. Key Features: Addresses various applications of Medical Big Data and Internet of Medical Things in real time environment Highlights recent innovations, designs, developments and topics of interest in machine learning techniques for classification of medical data Provides background and solutions to existing challenges in Medical Big Data and Internet of Medical Things Provides optimization techniques and programming models to parallelize the computationally intensive tasks in data mining of medical data Discusses interactions, advantages, limitations, challenges and future perspectives of IoT based remote healthcare monitoring systems. Includes data privacy and security analysis of cryptography methods for the Web of Medical Things (WoMT) Presents case studies on the next generation medical chair, electronic nose and pill cam are also presented. Content: Big data mining methods in medical applications / Laura Elezabeth, Ved P. Mishra -- Approaches in health care using big data and soft computing / Kunal Kabi, Jyotiprakash Panigrahi, Bhabani Shankar Prasad Mishra, Manas Kumar Rath, Satya Ranjan Dash -- Implantable electronics : integration of bio-interfaces, devices and sensors / Vinay Chowdary, Vivek Kaundal, Dr. Paawan Sharma, Dr. Amit Kumar Mondal -- Challenges in designing software architectures for web based biomedical signal analysis / Alan Jovic, Kresimir Jozic, Davor Kukolja, Kresimir Friganovic, Mario Cifrek -- Handling of medical imbalanced big data sets for improved classification using adjacent extreme mix neigbours over sampling technique (AEMNOST) / Sachin Patil, Shefali Sonavane -- A big data framework for removing misclassified instances based on fuzzy rough / Mai Abdrabo, Mohammed Elmogy, Ghada Eltaweel, Sherief Barakat -- Fuzzy C-mean and density-based spatial clustering for internet of things data processing / Heba El-Zeheiry, Mohammed Elmogy, Nagwa Elaraby, Sherif Barakat -- Parallel data mining techniques for breast cancer prediction / Bhavani R., Sudha Sadasivam G. -- A mapreduce approach to analysing healthcare big data / Golam Sarowar, Shamim Ripon, Fahim Rasel -- IoT and robotics in healthcare / Varnita Verma, Vinay Chowdary, Dr. Amit Kumar Mondal, Dr. Mukul Kumar Gupta -- Internet of medical things : remote healthcare and health monitoring perspective / Sitaramanjaneya Reddy Guntur, Rajani Reddy Gorrepati, Vijaya R. Dirisala -- A comparative analysis of classical cryptography vs. quantum cryptography for web of medical things (WoMT) / Anitha Kumari K., Sudha Sadasivam G. This book addresses recent advances in mining, learning, and analysis of big volume of medical images. The book presents taxonomies, trends and issues such as veracity in distributive, dynamic, and diverse data collection, data management, data models, hypotheses testing, training, validation, model-building, optimization techniques and governance of medical big data collected from multiple, heterogeneous IoT devises, networks, platforms and systems such as private vs. public cloud. The book includes privacy, trust, and security issues related to medical Big Data and related IoT and presents case studies in healthcare analytics as well This book addresses recent advances in data mining, learning, and analysis of big volume medical images resulting at a high rate from both real time systems and off line systems. The book includes privacy, trust, and security issues related to medical Big Data and related IoT and presents case studies in healthcare analytics as well.