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.
- 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.
Introduction to Medical Big Data Analytics. Introduction to IoT Devices and Health Bioinformatics. Part A: IoT in Life Sciences. 1. IoT and Robotics in Healthcare. 2. Implantable Electronics: Integration of Bio-interfaces, Devices and Sensors. 3. Electronic Devices, Circuits and Systems for Non-Invasive Diagnosis. 4. Internet of Things for Remote Healthcare and Health Monitoring. 5. Medical Electronics, Biomedical Instrumentations. 6. Surface Imaging for Bio-medical Applications. 7. Radiofrequency Devices, Circuits and Systems for e-Medicine. 8. Network Architectures and Frameworks for IoT Medical Applications. 9. Medical Big Data Management Systems and Infrastructures. Part B: Telemedicine and Health Care. 10. Disease Management, Auto-Administer Therapies. 11. Recommender Systems and Decision Support Systems. 12. Human Machine Interfaces. 13. Telemedicine and Mobile Applications- Healthcare. Part C: Medical Big Data Mining and Processing. 11. Big Data Mining Methods in Medical Applications. 12. Pattern Recognition, Features Extraction, Feature Reduction and Selection Techniques in Biomedical Applications.13. Classifiers in Biomedical and Healthcare Applications. Part D: Case studies for Classification in Medical Problems. 14. Applications. 15. Privacy and Security Issues in Big Data. 16. Standards, Challenges, and Recommendations for Advanced Classifiers in Medical Applications.