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Synergizing Data Envelopment Analysis and Machine Learning for Performance Optimization in Healthcare - Tapa blanda
Sinopsis
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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|ADEYEMI ABEL AJIBESIN - Editor|Associate Professor Adeyemi Abel Ajibesin holds a PhD in Computer Engineering from the University of Cape Town, South Africa, and another from Modibbo Adama University of Technology, Nigeria. He has over two decades of extensive experience in research, teaching, and leadership. He is currently a faculty member in the Department of IT at the Cape Peninsula University of Technology in South Africa. Previously, he held positions such as Chair/Head of Department (HoD) of Software Engineering in the School of IT & Computing (SITC) at the American University of Nigeria, founding Interim Dean of the School of Engineering, Interim Dean of the School of IT & Computing, and Director of the African Centre for ICT Innovation and Training. He has also served in various capacities at Simon Fraser University in Canada, the University of Cape Town in South Africa, and the Pan African University Institute for Basic Sciences, Technology & Innovation in Kenya, among other institutions. His research interests include Frontier Analysis – Data Envelopment Analysis, Artificial Intelligence, Machine Learning, Data Science & Analytics, Healthcare Informatics, Modeling and Simulation, and Broadband Networks. Dr. Ajibesin's prolific research portfolio is reflected in numerous articles published in reputable peer-reviewed conference proceedings, journals, and books, alongside patents for engineering products and copyrighted computing software, showcasing his innovative contributions to his field. His leadership and academic excellence have been recognized through numerous awards and grants, including esteemed accolades such as the Google Research, Google explorerCSR, AIMS, CSIR, and IEEE awards.
Senthil Kumar Thangavel I am currently working as a Professor at School of Computing, Coimbatore. I completed my Bachelors in B. Tech. (Computer Science and Engineering) from Sethu Institute of Technology, Madurai in 1999, and holding my Masters in M. Tech. (Distributed Computing Systems) from Pondicherry Engineering college, Pondicherry by 2004. I completed my PhD in Information and Communication Engineering from Anna University, Chennai in 2013. My research interests include Video Analytics, Big Data Analytics, Intrusion Detection Systems. I have around 73 Scopus publications. I have been working at Amrita Vishwa Vidyapeetham, Coimbatore since 2001. At Amrita I have Ph.D. scholars working in the area of Video Analytics, Intrusion detection system, Phishing identification, User behavioral analytics and Medical Image Processing. Apart from my scholars, I am the Co-Advisor or a Doctoral Committee member for other scholars at Amrita. I am involved in developing the skills in the competency areas of programming like MATLAB, NS2, JIST, C#, Android, Hadoop, Spark, OpenCV with Qt. My key focus is on developing semi-automated solutions that solves societal problems using Deep learning, Explainable AI and Transformer Models
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- EditorialIGI Global
- Año de publicación2025
- ISBN 13 9798337300825
- EncuadernaciónTapa blanda
- IdiomaInglés
- Número de páginas624
- EditorAjibesin Adeyemi Abel, G. Naveen Sundar, Thangavel Senthil Kumar
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Synergizing Data Envelopment Analysis and Machine Learning for Performance Optimization in Healthcare
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Synergizing Data Envelopment Analysis and Machine Learning for Performance Optimization in Healthcare
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
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Synergizing Data Envelopment Analysis and Machine Learning for Performance Optimization in Healthcare (Paperback)
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Paperback. Condición: new. Paperback. 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 UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9798337300825
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Synergizing Data Envelopment Analysis and Machine Learning for Performance Optimization in Healthcare
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
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Condición: New. Nº de ref. del artículo: 50323124-n
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Synergizing Data Envelopment Analysis and Machine Learning for Performance Optimization in Healthcare (Paperback)
Librería: AussieBookSeller, Truganina, VIC, Australia
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: new. Paperback. 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. Nº de ref. del artículo: 9798337300825
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