Synergizing Data Envelopment Analysis and Machine Learning for Performance Optimization in Healthcare - Hardcover
Synopsis
Healthcare systems face the challenge of delivering high-quality care while efficiently managing costs and resources. Traditional methods of performance evaluation often fall short when addressing the complex and diverse nature of healthcare operations. Data envelopment analysis (DEA) has been used to measure the efficiency of healthcare providers, but its linear, deterministic nature limits its adaptability to dynamic environments. In contrast, machine learning (ML) can handle complex, non-linear relationships and high-dimensional data, offering deeper insights and predictive capabilities. The synergy between DEA and ML presents an opportunity to overcome these limitations and drive more effective performance optimization. It leads to efficiency assessments through predictive analytics and improved resource allocation with data-driven insights and optimizing clinical pathways and decision support systems for better patient outcomes. Synergizing Data Envelopment Analysis and Machine Learning for Performance Optimization in Healthcare explores the integration of DEA and ML to enhance performance optimization in healthcare, improving efficiency, care quality, and resource management. It examines theoretical foundations, methodological innovations, and practical applications, providing a comprehensive resource with a key focus on development of algorithms to address challenges in healthcare optimization. Covering topics such as healthcare equipment manufacturing, human augmentation, and robotic surgery, this book is an excellent resource for hospital administrators, clinical managers, clinical decision-makers, policymakers, public health officials, professionals, researchers, scholars, academics, and more.
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About the Author
Senthil Kumar Thangavel completed his B.E(Computer Science and Engineering) from Sethu Institute of Technology, Madurai. He then completed his M.Tech(Distributed Computing Systems) from Pondicherry Engineering College, Pondicherry. He completed his Ph.D in Information and Communication Engineering from Anna University, Chennai. His areas of interest include video surveillance, Deep learning, cloud computing, software Engineering, Video processing, Wireless Sensor Networks, Big Data computing, Embedded Automation, and explainable AI. He works as an Associate Professor in the computer science and Engineering Department at Amrita School of Computing, Amrita Vishwa Vidyapeetham, Coimbatore. He is a reviewer for Elsevier Computers & Electrical Engineering Journal, IEEE Access, Inderscience, International Journal of Intelligent Information Systems IGI Global, Springer -Data Analysis, International Institute of Engineers Journal, Journal of Electronic Imaging, CMES: Computer Modeling in Engineering & Sciences. He has been working with Amrita Vishwa Vidyapeetham, Coimbatore, since 2001. He is a part of funded projects with IBM, DST, Ministry of Tribal Affairs, Telesto Energy Pvt Ltd, and Multicoreware.
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Synergizing Data Envelopment Analysis and Machine Learning for Performance Optimization in Healthcare (Hardcover)
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Hardcover. Condition: new. Hardcover. Healthcare systems face the challenge of delivering high-quality care while efficiently managing costs and resources. Traditional methods of performance evaluation often fall short when addressing the complex and diverse nature of healthcare operations. Data envelopment analysis (DEA) has been used to measure the efficiency of healthcare providers, but its linear, deterministic nature limits its adaptability to dynamic environments. In contrast, machine learning (ML) can handle complex, non-linear relationships and high-dimensional data, offering deeper insights and predictive capabilities. The synergy between DEA and ML presents an opportunity to overcome these limitations and drive more effective performance optimization. It leads to efficiency assessments through predictive analytics and improved resource allocation with data-driven insights and optimizing clinical pathways and decision support systems for better patient outcomes. Synergizing Data Envelopment Analysis and Machine Learning for Performance Optimization in Healthcare explores the integration of DEA and ML to enhance performance optimization in healthcare, improving efficiency, care quality, and resource management. It examines theoretical foundations, methodological innovations, and practical applications, providing a comprehensive resource with a key focus on development of algorithms to address challenges in healthcare optimization. Covering topics such as healthcare equipment manufacturing, human augmentation, and robotic surgery, this book is an excellent resource for hospital administrators, clinical managers, clinical decision-makers, policymakers, public health officials, professionals, researchers, scholars, academics, and more. "The objective of this book is to advance the field of healthcare performance optimization by exploring the integration of Data Envelopment Analysis (DEA) and Machine Learning (ML). The book will also explore advancements in data preprocessing, model interpretability, and the development of customized algorithms for healthcare applications"-- Provided by publisher. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798337300818
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Buch. Condition: Neu. Synergizing Data Envelopment Analysis and Machine Learning for Performance Optimization in Healthcare | Adeyemi Abel Ajibesin (u. a.) | Buch | Englisch | 2025 | IGI Global | EAN 9798337300818 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Seller Inventory # 133495478
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Synergizing Data Envelopment Analysis and Machine Learning for Performance Optimization in Healthcare (Hardcover)
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Hardcover. Condition: new. Hardcover. Healthcare systems face the challenge of delivering high-quality care while efficiently managing costs and resources. Traditional methods of performance evaluation often fall short when addressing the complex and diverse nature of healthcare operations. Data envelopment analysis (DEA) has been used to measure the efficiency of healthcare providers, but its linear, deterministic nature limits its adaptability to dynamic environments. In contrast, machine learning (ML) can handle complex, non-linear relationships and high-dimensional data, offering deeper insights and predictive capabilities. The synergy between DEA and ML presents an opportunity to overcome these limitations and drive more effective performance optimization. It leads to efficiency assessments through predictive analytics and improved resource allocation with data-driven insights and optimizing clinical pathways and decision support systems for better patient outcomes. Synergizing Data Envelopment Analysis and Machine Learning for Performance Optimization in Healthcare explores the integration of DEA and ML to enhance performance optimization in healthcare, improving efficiency, care quality, and resource management. It examines theoretical foundations, methodological innovations, and practical applications, providing a comprehensive resource with a key focus on development of algorithms to address challenges in healthcare optimization. Covering topics such as healthcare equipment manufacturing, human augmentation, and robotic surgery, this book is an excellent resource for hospital administrators, clinical managers, clinical decision-makers, policymakers, public health officials, professionals, researchers, scholars, academics, and more. "The objective of this book is to advance the field of healthcare performance optimization by exploring the integration of Data Envelopment Analysis (DEA) and Machine Learning (ML). The book will also explore advancements in data preprocessing, model interpretability, and the development of customized algorithms for healthcare applications"-- Provided by publisher. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9798337300818
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