Machine Intelligence for Internet of Medical Things: Applications and Future Trends | PDF Download

Description:

This book presents use-cases of IoT, AI and Machine Learning (ML) for healthcare delivery and medical devices. It compiles 15 topics that discuss the applications, opportunities, and future trends of machine intelligence in the medical domain. The objective of the book is to demonstrate how these technologies can be used to keep patients safe and healthy and, at the same time, to empower physicians to deliver superior care.

Readers will be familiarized with core principles, algorithms, protocols, emerging trends, security problems, and the latest concepts in e-healthcare services. It also includes a quick overview of deep feed forward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, practical methodology, and how they can be used to provide better solutions to healthcare related issues. The book is a timely update for basic and advanced readers in medicine, biomedical engineering, and computer science.

Key topics covered in the book:
– An introduction to the concept of the Internet of Medical Things (IoMT)
– Cloud-edge based IoMT architecture and performance optimization in the context of Medical Big Data
– A comprehensive survey on different IoMT interference mitigation techniques for Wireless Body Area Networks (WBANs)
– Artificial Intelligence and the Internet of Medical Things
– A review of new machine learning and AI solutions in different medical areas.
– A Deep Learning based solution to optimize obstacle recognition for visually impaired patients
– A survey of the latest breakthroughs in Brain-Computer Interfaces and their applications
– Deep Learning for brain tumor detection
– Blockchain and patient data management

PREFACE

The growing development in the field of computing has encouraged the integration of a variety of sophisticated devices inside houses and facilities. These devices communicate with each other to help users in particular situations and according to their needs, such as safety, comfort, and even health. The devices form an object connection environment known as the Internet of Things (IoT). Healthcare professionals are now embracing the Internet of Medical Things (IoMT), which refers to a connected infrastructure of devices and software applications that can communicate with various healthcare IT systems. One of these technologies — Remote Patient Monitoring — is commonly used for the treatment and care of patients.

Often associated with the IoT, Artificial Intelligence (AI) opens the field of possibilities in the medical area, in particular, by allowing the development of new diagnostic and interpretation tools of exceptional reliability and by assessing the large volumes of data that can be generated through the networks by sensors and users.

OBJECTIVE OF THE BOOK

The objective of this book is to focus on how to use IoT, AI and Machine Learning (ML), to keep patients safe and healthy and, at the same time, to empower physicians to deliver superlative care.

This book discusses the applications, opportunities, and future trends of machine intelligence in the medical domain, including both basic and advanced topics.

This book provides core principles, algorithms, protocols, emerging trends, security problems, and the latest e-healthcare services findings. It also includes deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, practical methodology, and how they can be used to provide better solutions to healthcare-related issues.

ORGANIZATION OF THE BOOK

Chapters 1-3: The authors introduce the concept of the Internet of Medical Things (IoMT), its roles, challenges, and the opportunities it may present to the healthcare system.

Chapter 4: The authors present a cloud-edge-based IoMT architecture and discuss the performance optimization it may provide in the context of Medical Big Data.

Chapter 5: The authors provide a comprehensive survey on different IoMT interference mitigation techniques for Wireless Body Area Networks (WBANs).

Chapters 6 and 7: The authors explore the possibilities that Artificial Intelligence and the Internet of Things can provide to prevent future pandemics.

Chapters 8-10: The authors provide a comprehensive review of the newest Machine Learning based solutions in different medical areas.

Chapter 11: The authors go through the latest discoveries in curing cardiovascular diseases by implementing Artificial Intelligence in healthcare settings.

Chapter 12: The authors propose a Deep Learning based solution to optimize obstacle recognition for visually impaired patients.

Chapter 13: The authors provide a survey on the latest breakthroughs in Brain-Computer Interfaces and their applications.

Chapter 14: The authors propose a solution to optimize the performance of Deep Learning for brain tumor detection.

Chapter 15: The authors explore the possibilities that Blockchain may offer inpatient data management.