The healthcare sphere is becoming more interconnected, intelligent and data driven. The management of healthcare data - and developments in the devices, technologies and systems used to obtain, interpret, store and access it - is a key theme for many researchers in healthcare and technology fields. Sensors and IoT devices are increasingly used to obtain better information about patients' health, machine learning and numerical optimization techniques are being deployed in interpreting medical information, and secure healthcare records and telemedicine approaches are needed to enable clinicians and patients to access data remotely. All these technological advances fall under the umbrella term of Healthcare 4.0.
This book explores the ideas and practices around utilizing IoT integration within the healthcare ecosystem to improve patient diagnosis, monitoring, and treatment, using machine learning and optimization to develop proactive healthcare systems. The book aims to provide both the necessary theoretical foundations for a sound understanding of the topic, and experimental case studies to show how Healthcare 4.0 applies to real-world situations. Coverage includes data modelling, information discovery, prediction, smart healthcare, transparency in governance, and auditing.
The book's editors have brought together an international team of experts to discuss and share their opinions on Healthcare 4.0, evaluate existing solutions and provide real-world, practical case studies. Their intention has been to provide an interesting resource for researchers in the field of computer science, computer engineering, healthcare technology, computer vision, pattern recognition, machine learning, IoT, AI, signal processing, blockchain and big data and those in related disciplines.
Saurav Mallik is a research scientist at R Ken Coit College of Pharmacy, University of Arizona, USA. Previously he worked as a postdoc at Harvard University, University of Texas, University of Miami, USA. He is an active member of IEEE, ACM and AACR, USA. He has authored more than 240 peer-reviewed research articles. His research areas include data mining, computational biology, bioinformatics, bio-statistics and machine learning.
Ben Othman Soufiane is an assistant professor of computer science at Applied College, the King Faisal University, Saudi Arabia from 2025. He has published more than 140 journal articles, conference papers and book chapters. He is an editorial board member of multiple international journals and has served as a technical program committee member for more than a dozen international conferences.
Junichi Iwata is a professor at University of Michigan School of Dentistry, USA. Previously, he was professor at the University of Texas Health Science Center, USA. He has authored more than 110 peer-reviewed research articles. His research areas include craniofacial development, genetics, noncoding RNAs and autophagy.
Subramaniam Karuppiah Barathi Sangeetha is an associate professor at Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India. She has 16 years of teaching experience and 10 years of research experience. She is a Life Member of the ISTE and IEI and has published more than 60 journal articles and conference papers, as well as 10 book chapters and 10 IPR patents in machine learning and deep learning.
Venkatesan Muthukumaran is an assistant professor at the SRM Institute of Science and Technology, Kattankulathur, India. He is a fellow of the International Association for Cryptologic Research (IACR), India and a life member of the IEEE. He has published more than 100 journal articles and conference papers, 10 book chapters and 10 IPR patents in algebraic with IoT applications. His research areas include machine learning, data science, blockchain, IoT, data mining, and algebraic cryptography.
