AI's impact on human societies is and will be drastic in so many ways. AI is being adopted and implemented around the world, and government and universities are investing in AI studies, research, and development. However, very little research exists about the impact of AI on our lives. This book will address this gap; it will gather reflections from around the world to assess the impact of AI on different aspects of society as well as propose ways in which we can address this impact and the research agendas needed.

Virtual communities have gained popularity in many growing fields and have continued to expand into healthcare environments. Analyzing the impact these communities have can help provide more effective methods to support patients and community members. Novel Applications of Virtual Communities in Healthcare Settings is a crucial scholarly reference source that examines the challenges virtual communities can face, as well as the advantages they provide to members of healthcare organizations. Featuring pertinent topics that include evaluation frameworks, disaster management, knowledge translation, and user engagement, this book is ideal for medical practitioners, academicians, students, and healthcare researchers that are interested in taking part in the latest discussions of virtual communities within medical fields.

This book offers a practical introduction to healthcare analytics that does not require a background in data science or statistics. It presents the basics of data, analytics and tools and includes multiple examples of their applications in the field. The book also identifies practical challenges that fuel the need for analytics in healthcare as well as the solutions to address these problems. In the healthcare field, professionals have access to vast amount of data in the form of staff records, electronic patient record, clinical findings, diagnosis, prescription drug, medical imaging procedure, mobile health, resources available, etc. Managing the data and analyzing it to properly understand it and use it to make well-informed decisions can be a challenge for managers and health care professionals. A new generation of applications, sometimes referred to as end-user analytics or self-serve analytics, are specifically designed for non-technical users such as managers and business professionals. The ability to use these increasingly accessible tools with the abundant data requires a basic understanding of the core concepts of data, analytics, and interpretation of outcomes. This book is a resource for such individuals to demystify and learn the basics of data management and analytics for healthcare, while also looking towards future directions in the field.

Healthcare providers require timely and accurate information about their patients. As such, a great amount of effort and resources are spent to ensure that the right information is presented to the right people at the right time. Research Perspectives on the Role of Informatics in Health Policy and Management focuses on the advancements of Health Information Science in order to solve current and forthcoming problems in the health sector. Managers, policy makers, researchers, and Masters and PhD students in healthcare related fields will use this book to provide necessary insight on healthcare delivery and also to inspire new ideas and practices to effectively provide patients with the greatest quality care.

This book provides a hands-on introduction to Machine Learning (ML) from a multidisciplinary perspective that does not require a background in data science or computer science. It explains ML using simple language and a straightforward approach guided by real-world examples in areas such as health informatics, information technology, and business analytics. The book will help readers understand the various key algorithms, major software tools, and their applications. Moreover, through examples from the healthcare and business analytics fields, it demonstrates how and when ML can help them make better decisions in their disciplines. The book is chiefly intended for undergraduate and graduate students who are taking an introductory course in machine learning. It will also benefit data analysts and anyone interested in learning ML approaches.

Emergent technologies, including ambient intelligence and pervasive computing, promise a considerable advance in the way people use virtual communities, and new, innovative applications are making virtual communities more dynamic and usable than ever. Virtual Community Building and the Information Society: Current and Future Directions offers a holistic approach to virtual communities, providing relevant theoretical frameworks and presenting the latest empirical research on virtual technology, infrastructures, content modeling, knowledge modeling, content management, context awareness, mobility, security and trust. It also explores the social impact and applications of virtual communities, providing valuable insights for professionals, researchers, and managers in fields including information systems, computer science, knowledge management, software engineering, healthcare, business, information and communication sciences, education, and sociology who want to improve their understanding of the strategic role of virtual communities in the information society.