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A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.
Archives and the Computer deals with the use of the computer and its systems and programs in archiving data and other related materials. The book covers topics such as the scope of automated systems in archives; systems for records management, archival description, and retrieval; and machine-readable archives. The selection also features examples of archives from different institutions such as the University of Liverpool, Berkshire County Record Office, and the National Maritime Museum.The text is recommended for archivists who would like to know more about the use of computers in archiving of records and other related information.
Drawing on the expertise of nineteen highly regarded American archivists, 'Managing Archives and Archival Institutions' establishes general principles that will be of practical value to archivists at all stages of professional development in all types of archival institutions. Contributions reflect the broad scope of archival work today and the wide range of skills and expertise archivists must acquire to meet the challenges presented by modern records and archives.
An archival collection is a unique body of information, created at a particular time by a particular organization or individual as a result of a particular activity. If the cultural record contained in an archive is to be accessible, the archivist must examine, organize, and describe each collection individually. Introduction to Archival Organization and Description guides the novice to an understanding of the nature of archival information and documentation. Chapters cover topics such as the characteristics of archival materials, the gathering and analysis of information for archival description, and the implementation of descriptive tools in information systems. The Introduction to series acquaints professionals and students with the complex issues and technologies in the production, management, and dissemination of cultural heritage information resources.
This book constitutes the refereed post-conference proceedings of the First International Conference on Data and Information in Online Environments, DIONE 2020, which took place in Florianópolis, Brazil, in March 2020. DIONE 2020 handles the growing interaction between the information sciences, communication sciences and computer sciences. The 18 revised full papers were carefully reviewed and selected from 37 submissions and focus on the production, dissemination and evaluation of contents in online environments. The goal is to improve cooperation between data science, natural language processing, data engineering, big data, research evaluation, network science, sociology of science and communication communities.
Intended to provide the basic foundation for modern archival practice and theory.
This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.