Main description:

The Internet of Medical Things (IoMT) allows clinicians to monitor patients remotely via a network of wearable or implantable devices. The devices are embedded with software or sensors to enable them to send and receive data via the internet so that healthcare professionals can monitor health data such as vital statistics, metabolic rates or drug delivery regimens, and can provide advice or treatment plans based on this real-world, real-time data. This edited book discusses key IoT technologies that facilitate and enhance this process, such as computer algorithms, network architecture, wireless communications, and network security.

Providing a systemic review of trends, challenges and future directions of IoMT technologies, the book examines applications such as breast cancer monitoring systems, patient-centric systems for handling, tracking and monitoring virus variants, and video-based solutions for monitoring babies. The book discusses machine learning techniques for the management of clinical data and includes security issues such as the use of blockchain technology.

Written by a range of international researchers, this book is a great resource for computer engineering researchers and practitioners in the fields of data mining, machine learning, artificial intelligence and the IoT in the healthcare sector.

Contents:

Chapter 1: Internet of medical things (IoMT): a systematic review of applications, trends, challenges, and future directions
Chapter 2: Non-invasive psycho-physiological driver monitoring through IoT-oriented systems
Chapter 3: IoT-based biomedical healthcare approach
Chapter 4: Impact of world pandemic "COVID-19" and an assessment of world health management and economics
Chapter 5: Artificial intelligence in healthcare
Chapter 6: Blockchain in IoT healthcare: case study
Chapter 7: Adaptive dictionary-based fusion of multi-modal images for health care applications
Chapter 8: Artificial intelligence for sustainable e-Health
Chapter 9: An innovative IoT-based breast cancer monitoring system with the aid of machine learning approach
Chapter 10: Patient-centric smart health-care systems for handling COVID-19 variants and future pandemics: technological review, research challenges, and future directions
Chapter 11: Application of intelligent techniques in health-care sector
Chapter 12: Managing clinical data using machine learning techniques
Chapter 13: Use of IoT and mobile technology in virus outbreak tracking and monitoring
Chapter 14: Video-based solutions for newborn monitoring
Chapter 15: IoT sensor networks in healthcare
Chapter 16: Machine learning for Healthcare 4.0: technologies, algorithms, vulnerabilities, and proposed solutions
Chapter 17: Big data analytics and data mining for healthcare and smart city applications