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This workshop proceedings advances international discussion of the opportunities and challenges, as well as successful strategies, for sharing and linking the massive amounts of population-based health and health care data that are routinely collected.
As the largest generation in U.S. history - the population born in the two decades immediately following World War II - enters the age of risk for cognitive impairment, growing numbers of people will experience dementia (including Alzheimer's disease and related dementias). By one estimate, nearly 14 million people in the United States will be living with dementia by 2060. Like other hardships, the experience of living with dementia can bring unexpected moments of intimacy, growth, and compassion, but these diseases also affect people's capacity to work and carry out other activities and alter their relationships with loved ones, friends, and coworkers. Those who live with and care for individuals experiencing these diseases face challenges that include physical and emotional stress, difficult changes and losses in their relationships with life partners, loss of income, and interrupted connections to other activities and friends. From a societal perspective, these diseases place substantial demands on communities and on the institutions and government entities that support people living with dementia and their families, including the health care system, the providers of direct care, and others. Nevertheless, research in the social and behavioral sciences points to possibilities for preventing or slowing the development of dementia and for substantially reducing its social and economic impacts. At the request of the National Institute on Aging of the U.S. Department of Health and Human Services, Reducing the Impact of Dementia in America assesses the contributions of research in the social and behavioral sciences and identifies a research agenda for the coming decade. This report offers a blueprint for the next decade of behavioral and social science research to reduce the negative impact of dementia for America's diverse population. Reducing the Impact of Dementia in America calls for research that addresses the causes and solutions for disparities in both developing dementia and receiving adequate treatment and support. It calls for research that sets goals meaningful not just for scientists but for people living with dementia and those who support them as well. By 2030, an estimated 8.5 million Americans will have Alzheimer's disease and many more will have other forms of dementia. Through identifying priorities social and behavioral science research and recommending ways in which they can be pursued in a coordinated fashion, Reducing the Impact of Dementia in America will help produce research that improves the lives of all those affected by dementia.
Society is now completely driven by data with many industries relying on data to conduct business or basic functions within the organization. With the efficiencies that big data bring to all institutions, data is continuously being collected and analyzed. However, data sets may be too complex for traditional data-processing, and therefore, different strategies must evolve to solve the issue. The field of big data works as a valuable tool for many different industries. The Research Anthology on Big Data Analytics, Architectures, and Applications is a complete reference source on big data analytics that offers the latest, innovative architectures and frameworks and explores a variety of applications within various industries. Offering an international perspective, the applications discussed within this anthology feature global representation. Covering topics such as advertising curricula, driven supply chain, and smart cities, this research anthology is ideal for data scientists, data analysts, computer engineers, software engineers, technologists, government officials, managers, CEOs, professors, graduate students, researchers, and academicians.
With the advent of new technologies in big data science, the study of medical problems has made significant progress. Connecting medical studies and computational methods is crucial for the advancement of the medical industry. Big Data Analytics in HIV/AIDS Research provides emerging research on the development and implementation of computational techniques in big data analysis for biological and medical practices. While highlighting topics such as deep learning, management software, and molecular modeling, this publication explores the various applications of data analysis in clinical decision making. This book is a vital resource for medical practitioners, nurses, scientists, researchers, and students seeking current research on the connections between data analytics in the field of medicine.
This book examines the large and growing human and financial cost of dementia and discusses policy options for improving care, controlling costs, and facilitating research.
OECD Insights: Ageing discusses the problems, challenges, and opportunities that ageing brings to citizens and governments in developed and developing countries.
The number of older subjects is rapidly increasingly worldwide. As a consequence, the nature of clinical conditions is also changing. Traditional medicine and models of care have been based on the evaluation and treatment of single and usually acute conditions occurring in relatively young individuals. Today, the usual clinical manifestation of diseases is characterized by multiple and often chronic conditions affecting older people. In this scenario, frailty and dementia have been triggering special interest both in research and clinical settings due to their high prevalence, impact on the individual’s quality of life, and consequences for public health worldwide. These conditions aptly reflect the complexity of age-related pathological conditions, finding as causal factor a myriad of heterogeneous, interacting, and often still unclear pathophysiological processes. Indeed, their study is strongly affected by the difficulty to differentiate the effects of a normal aging process from eventual pathological deviations of the underlying systems. Their occurrence and trajectories over time are strongly affected by a wide array of factors and determinants that can be hardly attributed to the deficit/involvement of single biological systems and/or health domains. Moreover, environment and social factors also play a key role in the determination of phenotypes. The present Research Topic is aimed at widening our understanding of the frailty and dementia phenomena occurring with aging, in order to improve the clinical and public health approaches to these burdening conditions.
This report discusses the need for an integrated and cyclical approach to managing health technology in order to mitigate clinical and financial risks, and ensure acceptable value for money.
Many new pieces of technology can be beneficial to individuals living with dementia, including both hardware and software. This straightforward guide summarises the current research on this growing topic, and gives practical advice on how available technology can be used to improve the everyday lives of people with dementia. Looking at a range of available products, such as off-the-shelf computers and smartphones, to dementia specific applications and programs, it also addresses some common obstacles and barriers faced when introducing technology in dementia care. The past twenty years have seen an array of technologies developed to improve the day-to-day lives of people with dementia; this guide shows how they can be effectively used.
Big Data in Psychiatry and Neurology provides an up-to-date overview of achievements in the field of big data in Psychiatry and Medicine, including applications of big data methods to aging disorders (e.g., Alzheimer's disease and Parkinson's disease), mood disorders (e.g., major depressive disorder), and drug addiction. This book will help researchers, students and clinicians implement new methods for collecting big datasets from various patient populations. Further, it will demonstrate how to use several algorithms and machine learning methods to analyze big datasets, thus providing individualized treatment for psychiatric and neurological patients. As big data analytics is gaining traction in psychiatric research, it is an essential component in providing predictive models for both clinical practice and public health systems. As compared with traditional statistical methods that provide primarily average group-level results, big data analytics allows predictions and stratification of clinical outcomes at an individual subject level. - Discusses longitudinal big data and risk factors surrounding the development of psychiatric disorders - Analyzes methods in using big data to treat psychiatric and neurological disorders - Describes the role machine learning can play in the analysis of big data - Demonstrates the various methods of gathering big data in medicine - Reviews how to apply big data to genetics