Sinopsis
Multiple-criteria decision making, including multiple rule-based decision making, multiple-objective decision making, and multiple-attribute decision making, is used by clinical decision makers to analyze healthcare issues from various perspectives. In practical healthcare cases, semi-structured and unstructured decision-making issues involve multiple criteria that may conflict with each other. Thus, the use of multiple-criteria decision making is a promising source of practical solutions for such problems. AI-Enabled Multiple-Criteria Decision-Making Approaches for Healthcare Management investigates the contributions of practical multiple-criteria decision analysis applications and cases for healthcare management. The book also considers the best practices and tactics for utilizing multiple-criteria decision making to ensure the technology is utilized appropriately. Covering key topics such as fuzzy data, augmented reality, blockchain, and data transmission, this reference work is ideal for computer scientists, healthcare professionals, nurses, policymakers, researchers, scholars, academicians, practitioners, educators, and students.
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AI-Enabled Multiple-Criteria Decision-Making Approaches for Healthcare Management
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Gebunden. Condición: New. In practical health care cases, semi-structured and unstructured decision-making issues involve multiple criteria (or goals) that may conflict with each other. Thus, the use of MCDM is a promising source of practical solutions for such problems.Klap. Nº de ref. del artículo: 596670165
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AI-Enabled Multiple Criteria Decision-Making Approaches for Healthcare Management
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Hardback. Condición: New. The success of healthcare decision-making lies in whether healthcare staff, patients, and healthcare organization managers can comprehensively understand the choices and consider future implications to make the best decision possible. Multiple-criteria decision making (MCDM), including multiple rule-based decision making (MRDM), multiple-objective decision making (MODM), and multiple-attribute decision making (MADM), is used by clinical decision-makers to analyze healthcare issues from various perspectives.In practical health care cases, semi-structured and unstructured decision-making issues involve multiple criteria (or goals) that may conflict with each other. Thus, the use of MCDM is a promising source of practical solutions for such problems. MCDM methods mainly include the three parts: data process, evaluation and selection, and planning and design. Data process focuses on analyzing and identifying healthcare management issues and data features for solving practical cases. Evaluation and selection focus on evaluating the performance of each solution for healthcare management, and these methods can be used to support decision-making and help organizations choose the best solution for practical healthcare management cases. Finally, planning and design focus on analyzing and designing the goals of healthcare management applications, which can be modelled as a minimizing or maximizing problem for finding the optimal solutions. Furthermore, these methods can explore the relationship structure construction among criteria between various related issues arising from healthcare. Nº de ref. del artículo: LU-9781668444054
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AI-Enabled Multiple Criteria Decision-Making Approaches for Healthcare Management
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Hardback. Condición: New. The success of healthcare decision-making lies in whether healthcare staff, patients, and healthcare organization managers can comprehensively understand the choices and consider future implications to make the best decision possible. Multiple-criteria decision making (MCDM), including multiple rule-based decision making (MRDM), multiple-objective decision making (MODM), and multiple-attribute decision making (MADM), is used by clinical decision-makers to analyze healthcare issues from various perspectives.In practical health care cases, semi-structured and unstructured decision-making issues involve multiple criteria (or goals) that may conflict with each other. Thus, the use of MCDM is a promising source of practical solutions for such problems. MCDM methods mainly include the three parts: data process, evaluation and selection, and planning and design. Data process focuses on analyzing and identifying healthcare management issues and data features for solving practical cases. Evaluation and selection focus on evaluating the performance of each solution for healthcare management, and these methods can be used to support decision-making and help organizations choose the best solution for practical healthcare management cases. Finally, planning and design focus on analyzing and designing the goals of healthcare management applications, which can be modelled as a minimizing or maximizing problem for finding the optimal solutions. Furthermore, these methods can explore the relationship structure construction among criteria between various related issues arising from healthcare. Nº de ref. del artículo: LU-9781668444054
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Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Multiple-criteria decision making, including multiple rule-based decision making, multiple-objective decision making, and multiple-attribute decision making, is used by clinical decision makers to analyze healthcare issues from various perspectives. In practical healthcare cases, semi-structured and unstructured decision-making issues involve multiple criteria that may conflict with each other. Thus, the use of multiple-criteria decision making is a promising source of practical solutions for such problems. AI-Enabled Multiple-Criteria Decision-Making Approaches for Healthcare Management investigates the contributions of practical multiple-criteria decision analysis applications and cases for healthcare management. The book also considers the best practices and tactics for utilizing multiple-criteria decision making to ensure the technology is utilized appropriately. Covering key topics such as fuzzy data, augmented reality, blockchain, and data transmission, this reference work is ideal for computer scientists, healthcare professionals, nurses, policymakers, researchers, scholars, academicians, practitioners, educators, and students. Nº de ref. del artículo: 9781668444054
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