Intelligent and adaptive techniques are rapidly being used in all stages of medical treatment, from the initial diagnosis to planning delivery and follow-up therapy.To realize the full potential of these techniques, developers and end users must understand both the underlying technology and the specifics of the medical application considered. Focus
"This book explores the foundation, history, and theory of intelligent adaptive systems, providing a fundamental resource on topics such as the emergence of intelligent adaptive systems in social sciences, biologically inspired artificial social systems, sensory information processing, as well as the conceptual and methodological issues and approaches to intelligent adaptive systems"--Provided by publisher.
As ubiquitous as the atmosphere, intelligent adaptive systems (IASs) surround us in our daily lives. When designed well, these systems sense users and their environments so that they can provide support in a manner that is not only responsive to the evolving situation, but unnoticed by the user. A synthesis of recent research and developments on IASs from the human factors (HF) and human–computer interaction (HCI) domains, Intelligent Adaptive Systems: An Interaction-Centered Design Perspective provides integrated design guidance and recommendations for researchers and system developers. The book explores a recognized lack of integration between the HF and HCI research communities, which has led to inconsistencies between the research approaches adopted, and a lack of exploitation of research from one field by the other. The authors integrate theories and methodologies from these domains to provide design recommendations for human–machine developers. They then establish design guidance through the review of conceptual frameworks, analytical methodologies, and design processes for intelligent adaptive systems. The book draws on case studies from the military, medical, and distance learning domains to illustrate intelligent system design to examine lessons learned. Outlining an interaction-centered perspective for designing an IAS, the book details methodologies for understanding human work in complex environments and offers understanding about why and how optimizing human–machine interaction should be central to the design of IASs. The authors present an analytical and design methodology as well as an implementation strategy that helps you choose the proper design framework for your needs.
The book intends to cover various problematic aspects of emerging smart computing and self-adapting technologies comprising of machine learning, artificial intelligence, deep learning, robotics, cloud computing, fog computing, data mining algorithms, including emerging intelligent and smart applications related to these research areas. Further coverage includes implementation of self-adaptation architecture for smart devices, self-adaptive models for smart cities and self-driven cars, decentralized self-adaptive computing at the edge networks, energy-aware AI-based systems, M2M networks, sensors, data analytics, algorithms and tools for engineering self-adaptive systems, and so forth. Acts as guide to Self-healing and Self-adaptation based fully automatic future technologies Discusses about Smart Computational abilities and self-adaptive systems Illustrates tools and techniques for data management and explains the need to apply, and data integration for improving efficiency of big data Exclusive chapter on the future of self-stabilizing and self-adaptive systems of systems Covers fields such as automation, robotics, medical sciences, biomedical and agricultural sciences, healthcare and so forth This book is aimed researchers and graduate students in machine learning, information technology, and artificial intelligence.
"This book provides an estimable global view of the most up-to-date research on the strategies, applications, practice, and implications of complex adaptive systems, to better understand the various critical systems that surround human life. Researchers will find this book an indispensable state-of-art reference"--Provided by publisher.
Tomorrow's best physicians will be those who continually learn, adjust, and innovate as new information and best practices evolve, reflecting adaptive expertise in response to practice challenges. As the first volume in the American Medical Association's MedEd Innovation Series, The Master Adaptive Learner is an instructor-focused guide covering models for how to train and teach future clinicians who need to develop these adaptive skills and utilize them throughout their careers. - Explains and clarifies the concept of a Master Adaptive Learner: a metacognitive approach to learning based on self-regulation that fosters the success and use of adaptive expertise in practice. - Contains both theoretical and practical material for instructors and administrators, including guidance on how to implement a Master Adaptive Learner approach in today's institutions. - Gives instructors the tools needed to empower students to become efficient and successful adaptive learners. - Helps medical faculty and instructors address gaps in physician training and prepare new doctors to practice effectively in 21st century healthcare systems. - One of the American Medical Association Change MedEd initiatives and innovations, written and edited by members of the ACE (Accelerating Change in Medical Education) Consortium – a unique, innovative collaborative that allows for the sharing and dissemination of groundbreaking ideas and projects.
Second in a series of publications from the Institute of Medicine's Quality of Health Care in America project Today's health care providers have more research findings and more technology available to them than ever before. Yet recent reports have raised serious doubts about the quality of health care in America. Crossing the Quality Chasm makes an urgent call for fundamental change to close the quality gap. This book recommends a sweeping redesign of the American health care system and provides overarching principles for specific direction for policymakers, health care leaders, clinicians, regulators, purchasers, and others. In this comprehensive volume the committee offers: A set of performance expectations for the 21st century health care system. A set of 10 new rules to guide patient-clinician relationships. A suggested organizing framework to better align the incentives inherent in payment and accountability with improvements in quality. Key steps to promote evidence-based practice and strengthen clinical information systems. Analyzing health care organizations as complex systems, Crossing the Quality Chasm also documents the causes of the quality gap, identifies current practices that impede quality care, and explores how systems approaches can be used to implement change.
"This volume offers intriguing applications, reviews and additions to the methodology of intelligent computing, presenting the emerging trends of state-of-the-art intelligent systems and their practical applications"--Provided by publisher.
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
