When considering the idea of using machine learning in healthcare, it is a Herculean task to present the entire gamut of information in the field of intelligent systems. It is, therefore the objective of this book to keep the presentation narrow and intensive. This approach is distinct from others in that it presents detailed computer simulations for all models presented with explanations of the program code. It includes unique and distinctive chapters on disease diagnosis, telemedicine, medical imaging, smart health monitoring, social media healthcare, and machine learning for COVID-19. These chapters help develop a clear understanding of the working of an algorithm while strengthening logical thinking. In this environment, answering a single question may require accessing several data sources and calling on sophisticated analysis tools. While data integration is a dynamic research area in the database community, the specific needs of research have led to the development of numerous middleware systems that provide seamless data access in a result-driven environment. Since this book is intended to be useful to a wide audience, students, researchers and scientists from both academia and industry may all benefit from this material. It contains a comprehensive description of issues for healthcare data management and an overview of existing systems, making it appropriate for introductory and instructional purposes. Prerequisites are minimal; the readers are expected to have basic knowledge of machine learning. This book is divided into 22 real-time innovative chapters which provide a variety of application examples in different domains. These chapters illustrate why traditional approaches often fail to meet customers’ needs. The presented approaches provide a comprehensive overview of current technology. Each of these chapters, which are written by the main inventors of the presented systems, specifies requirements and provides a description of both the chosen approach and its implementation. Because of the self-contained nature of these chapters, they may be read in any order. Each of the chapters use various technical terms which involve expertise in machine learning and computer science.

Instructor Resources: Suggested case solutions (Word or Excel formats) Financial issues are of paramount importance in today's rapidly changing and increasingly competitive healthcare environment. Healthcare managers must understand accounting and financial management concepts and be prepared to operationalize them in their organizations. Healthcare Applications: A Casebook in Accounting and Financial Management provides a series of practice exercises for analyzing, understanding, and applying these concepts across a wide range of healthcare settings. Healthcare Applications contains 56 short cases designed to link theory to practical, real-world application via active learning. Based on fictitious entities and individuals (unless otherwise noted), the cases cover basic concepts--such as how to record transactions, compute financial ratios, and prepare financial statements--as well as more advanced issues, such as the effects of healthcare regulation, the valuation of debt or equity securities, cost-volume-profit analysis, and capital budgeting. Each case features assignments or questions to enhance students' critical thinking and generate classroom engagement. Instructors can select case topics and difficulty levels that are most appropriate for their courses. The book's cases can be either assigned as out-of-class homework or used directly in class to introduce a topic or facilitate discussion.

This book covers a wide range of topics on the role of Artificial Intelligence, Machine Learning, and Big Data for healthcare applications and deals with the ethical issues and concerns associated with it. This book explores the applications in different areas of healthcare and highlights the current research. "Big Data and Artificial Intelligence for Healthcare Applications" covers healthcare big data analytics, mobile health and personalized medicine, clinical trial data management and presents how Artificial Intelligence can be used for early disease diagnosis prediction and prognosis. It also offers some case studies that describes the application of Artificial Intelligence and Machine Learning in healthcare. Researchers, healthcare professionals, data scientists, systems engineers, students, programmers, clinicians, and policymakers will find this book of interest.

Web-based applications provide the power of desktop and server applications with the exibility and accessibility of the web. Using web browsers, users can securely access applications from anywhere within the reach of the company intranet or extranet. The special issue strives to explore the advanced web-based information systems and database applications in healthcare area. Healthcare organizations are undergoing major reorganizations and adjustments to meet the increasing demands of improved healthcare access and quality, as well as lowered costs. As the use of information technology to process medical data increases, much of the critical information necessary to meet these challenges is being stored in digital format. Web-enabled information technologies can provide the means for greater access and more effective integration of healthcare information from disparate computer applications and other information resources. This book presents studies from leading researchers and practitioners focusing on the current challenges, directions, trends, and opportunities associated with heal- care organizations and their strategic use of web-enabled technologies. Managing healthcare information systems with web-enabled technologies is an excellent ve- cle for understanding current and potential uses of Internet technology in the broad areas of healthcare and medical applications.

Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. Highlights different data techniques in healthcare data analysis, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks Includes applications and case studies across all areas of AI in healthcare data

Herbal Biomolecules in Healthcare Applications presents extensive detailed information on all the vital principles, basics and fundamental aspects of multiple herbal biomolecules in the healthcare industry. This book examines important herbal biomolecules including alkaloids, glycosides, flavonoids, anthraquinones, steroids, polysaccharides, tannins and polyphenolic compounds, terpenes, fats and waxes, proteins and peptides, and vitamins. These herbal biomacromolecules are responsible for different bioactivities as well as pharmacological potentials. A systematic understanding of the extraction, purification, characterization, applications of these herbal biomolecules and their derivatives in healthcare fields is developed in this comprehensive book. Chapters explore the key topics along with an emphasis on recent research and developments in healthcare fields by leading experts. They include updated literature review of the relevant key topics, good quality illustrations, chemical structures, flow charts, well-organized tables and case studies. Herbal Biomolecules in Healthcare Applications will be useful for researchers working on natural products and biomolecules with bioactivity and nutraceutical properties. Professionals specializing in scientific areas such as biochemistry, pharmacology, analytical chemistry, organic chemistry, clinics, or engineering focused on bioactive natural products will find this book useful. Provides a study of different type of biomolecules from herbal extracts and their bioactivities as well as their application in the healthcare industry Contributions by global leaders and experts from academia, industry and regulatory agencies, who have been considered as pioneers in the application of herbal biomolecules in the diverse healthcare fields Includes updated literature review along with practical examples and research case studies

"This book provides an in-depth introduction into medical, social, psychological, and technical aspects of smart healthcare applications as well as their consequences for the design, use and acceptance of future systems"--Provided by publisher.