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Aging Well : Solutions to the Most Pressing Global Challenges of Aging
Outlines the challenges of supporting the health and wellbeing of older adults around the world and offers examples of solutions designed by stakeholders, healthcare providers, and public, private and nonprofit organizations in the United States. The solutions presented address challenges including: providing person-centered long-term care, making palliative care accessible in all healthcare settings and the home, enabling aging-in-place, financing long-term care, improving care coordination and access to care, delivering hospital-level and emergency care in the home and retirement community settings, merging health and social care, supporting people living with dementia and their caregivers, creating communities and employment opportunities that are accessible and welcoming to those of all ages and abilities, and combating the stigma of aging. The innovative programs of support and care in Aging Well serve as models of excellence that, when put into action, move health spending toward a sustainable path and greatly contribute to the well-being of older adults.
Machine learning in healthcare : Fundamentals and recent applications
Discusses how to build various ML algorithms and how they can be applied to improve healthcare systems. Healthcare applications of AI are innumerable: medical data analysis, early detection and diagnosis of disease, providing objective-based evidence to reduce human errors, curtailing inter- and intra-observer errors, risk identification and interventions for healthcare management, real-time health monitoring, assisting clinicians and patients for selecting appropriate medications, and evaluating drug responses. Extensive demonstrations and discussion on the various principles of machine learning and its application in healthcare is provided, along with solved examples and exercises.
Machine learning and deep learning in medical data analytics and healthcare applications
Introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary machine learning (ML) and deep learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector, this book demonstrates the depth, breadth, complexity, and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/DL algorithms and explores the related use cases in enterprises such as computer-aided medical diagnostics, drug discovery and development, medical imaging, automation, robotic surgery, electronic smart records creation, outbreak prediction, medical image analysis, and radiation treatments.
