Federated Learning For Digital Healthcare Systems

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Federated Learning for Digital Healthcare Systems

Author : Agbotiname Lucky Imoize,Mohammad S Obaidat,Houbing Herbert Song
Publisher : Elsevier
Page : 459 pages
File Size : 54,5 Mb
Release : 2024-06-10
Category : Computers
ISBN : 9780443138966

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Federated Learning for Digital Healthcare Systems by Agbotiname Lucky Imoize,Mohammad S Obaidat,Houbing Herbert Song Pdf

Federated Learning for Digital Healthcare Systems critically examines the key factors that contribute to the problem of applying machine learning in healthcare systems and investigates how federated learning can be employed to address the problem. The book discusses, examines, and compares the applications of federated learning solutions in emerging digital healthcare systems, providing a critical look in terms of the required resources, computational complexity, and system performance. In the first section, chapters examine how to address critical security and privacy concerns and how to revamp existing machine learning models. In subsequent chapters, the book's authors review recent advances to tackle emerging efficient and lightweight algorithms and protocols to reduce computational overheads and communication costs in wireless healthcare systems. Consideration is also given to government and economic regulations as well as legal considerations when federated learning is applied to digital healthcare systems. Provides insights into real-world scenarios of the design, development, deployment, application, management, and benefits of federated learning in emerging digital healthcare systems Highlights the need to design efficient federated learning-based algorithms to tackle the proliferating security and patient privacy issues in digital healthcare systems Reviews the latest research, along with practical solutions and applications developed by global experts from academia and industry

Federated Learning

Author : Qiang Qiang Yang,Yang Yang Liu,Yong Yong Cheng,Yan Yan Kang,Tianjian Tianjian Chen,Han Han Yu
Publisher : Springer Nature
Page : 189 pages
File Size : 47,9 Mb
Release : 2022-06-01
Category : Computers
ISBN : 9783031015854

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Federated Learning by Qiang Qiang Yang,Yang Yang Liu,Yong Yong Cheng,Yan Yan Kang,Tianjian Tianjian Chen,Han Han Yu Pdf

How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.

Federated Learning and AI for Healthcare 5.0

Author : Hassan, Ahdi,Prasad, Vivek Kumar,Bhattacharya, Pronaya,Dutta, Pushan,Damaševi?ius, Robertas
Publisher : IGI Global
Page : 413 pages
File Size : 53,9 Mb
Release : 2023-12-18
Category : Medical
ISBN : 9798369310830

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Federated Learning and AI for Healthcare 5.0 by Hassan, Ahdi,Prasad, Vivek Kumar,Bhattacharya, Pronaya,Dutta, Pushan,Damaševi?ius, Robertas Pdf

The Healthcare sector is evolving with Healthcare 5.0, promising better patient care and efficiency. However, challenges like data security and analysis arise due to increased digitization. Federated Learning and AI for Healthcare 5.0 offers solutions, explaining cloud computing's role in managing data and advocating for security measures. It explores federated learning's use in maintaining data privacy during analysis, presenting practical cases for implementation. The book also addresses emerging tech like quantum computing and blockchain-based services, envisioning an innovative Healthcare 5.0. It empowers healthcare professionals, IT experts, and data scientists to leverage these technologies for improved patient care and system efficiency, making Healthcare 5.0 secure and patient centric.

Federated Learning Systems

Author : Muhammad Habib ur Rehman,Mohamed Medhat Gaber
Publisher : Springer Nature
Page : 207 pages
File Size : 42,8 Mb
Release : 2021-06-11
Category : Technology & Engineering
ISBN : 9783030706043

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Federated Learning Systems by Muhammad Habib ur Rehman,Mohamed Medhat Gaber Pdf

This book covers the research area from multiple viewpoints including bibliometric analysis, reviews, empirical analysis, platforms, and future applications. The centralized training of deep learning and machine learning models not only incurs a high communication cost of data transfer into the cloud systems but also raises the privacy protection concerns of data providers. This book aims at targeting researchers and practitioners to delve deep into core issues in federated learning research to transform next-generation artificial intelligence applications. Federated learning enables the distribution of the learning models across the devices and systems which perform initial training and report the updated model attributes to the centralized cloud servers for secure and privacy-preserving attribute aggregation and global model development. Federated learning benefits in terms of privacy, communication efficiency, data security, and contributors’ control of their critical data.

Federated Learning for Internet of Medical Things

Author : Pronaya Bhattacharya,Ashwin Verma,Sudeep Tanwar
Publisher : CRC Press
Page : 308 pages
File Size : 45,9 Mb
Release : 2023-06-16
Category : Computers
ISBN : 9781000891317

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Federated Learning for Internet of Medical Things by Pronaya Bhattacharya,Ashwin Verma,Sudeep Tanwar Pdf

This book intends to present emerging Federated Learning (FL)-based architectures, frameworks, and models in Internet of Medical Things (IoMT) applications. It intends to build on the basics of the healthcare industry, the current data sharing requirements, and security and privacy issues in medical data sharing. Once IoMT is presented, the book shifts towards the proposal of privacy-preservation in IoMT, and explains how FL presents a viable solution to these challenges. The claims are supported through lucid illustrations, tables, and examples that present effective and secured FL schemes, simulations, and practical discussion on use-case scenarios in a simple manner. The book intends to create opportunities for healthcare communities to build effective FL solutions around the presented themes, and to support work in related areas that will benefit from reading the book. It also intends to present breakthroughs and foster innovation in FL-based research, specifically in the IoMT domain. The emphasis of this book is on understanding the contributions of IoMT to healthcare analytics, and its aim is to provide insights including evolution, research directions, challenges, and the way to empower healthcare services through federated learning. The book also intends to cover the ethical and social issues around the recent advancements in the field of decentralized Artificial Intelligence. The book is mainly intended for undergraduates, post-graduates, researchers, and healthcare professionals who wish to learn FL-based solutions right from scratch, and build practical FL solutions in different IoMT verticals.

Intelligent Healthcare

Author : Chinmay Chakraborty,Mohammad R. Khosravi
Publisher : Springer Nature
Page : 493 pages
File Size : 44,5 Mb
Release : 2022-06-02
Category : Medical
ISBN : 9789811681509

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Intelligent Healthcare by Chinmay Chakraborty,Mohammad R. Khosravi Pdf

The book Intelligent Healthcare: Infrastructure, Algorithms, and Management® cover a wide range of research topics on innovative intelligent healthcare solutions and advancements with the latest research developments. Data analytics are relevant for healthcare to meet many technical challenges and issues that need to be addressed to realize this potential. The advanced healthcare systems have to be upgraded with new capabilities such as data analytics, machine learning, intelligent decision making, and more professional services. The Internet of Things helps to design and develop intelligent healthcare solutions assisted by security, data analytics, and machine learning. This book will provide federated learning, Data-driven infrastructure design, analytical approaches, and technological solutions with case studies for smart healthcare. This book aims to attract works on multidisciplinary research spanning across computer science and engineering, environmental studies, services, urban planning and development, Healthcare, social sciences, and industrial engineering on technologies, case studies, novel approaches, and visionary ideas related to data-driven innovative learning and computing solutions and big medical data-powered applications to cope with the real-world challenges for building smart healthcare sectors. Main Features: Ø Immersive technologies in healthcare Ø Internet of medical things Ø Federated learning algorithms Ø Explainable AI in Pervasive Healthcare Ø New management principles using biomedical data Ø Secured healthcare management systems This book aims to set up a better understanding of data scientists, researchers, and technologists under innovative digital health. The reader can find out existing research challenges, current market trends, and low-cost technologies to smoothly address the digital health issue.

Dimensions of Intelligent Analytics for Smart Digital Health Solutions

Author : Nilmini Wickramasinghe,Freimut Bodendorf,Mathias Kraus
Publisher : CRC Press
Page : 449 pages
File Size : 49,7 Mb
Release : 2024-03-01
Category : Medical
ISBN : 9781003849704

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Dimensions of Intelligent Analytics for Smart Digital Health Solutions by Nilmini Wickramasinghe,Freimut Bodendorf,Mathias Kraus Pdf

This title demystifies artificial intelligence (AI) and analytics, upskilling individuals (healthcare professionals, hospital managers, consultants, researchers, students, and the population at large) around analytics and AI as it applies to healthcare. This book shows how the tools, techniques, technologies, and tactics around analytics and AI can be best leveraged and utilised to realise a healthcare value proposition of better quality, better access and high value for everyone every day, everywhere. The book presents a triumvirate approach including technical, business and medical aspects of data and analytics and by so doing takes a responsible approach to this key area. This work serves to introduce the critical issues in AI and analytics for healthcare to students, practitioners, and researchers.

Federated Deep Learning for Healthcare

Author : Amandeep Kaur,Chetna Kaushal,MD Mehedi Hassan,Si Thu Aung
Publisher : Unknown
Page : 0 pages
File Size : 41,5 Mb
Release : 2024-10-02
Category : Computers
ISBN : 1032689552

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Federated Deep Learning for Healthcare by Amandeep Kaur,Chetna Kaushal,MD Mehedi Hassan,Si Thu Aung Pdf

This book provides a practical guide to federated deep learning for healthcare including fundamental concepts, framework, and the applications comprising of domain adaptation, model distillation, and transfer learning. It covers concerns in model fairness, data bias, regulatory compliance, and ethical dilemmas. It investigates several privacy-preserving methods like homomorphic encryption, secure multi-party computation, and differential privacy. It will enable readers to build and implement federated learning systems that safeguard private medical information. Features: - Offers a thorough introduction of federated deep learning methods designed exclusively for medical applications. - Investigates privacy-preserving methods with emphasis on data security and privacy. - Discusses healthcare scaling and resource efficiency considerations. - Examines methods for sharing information among various healthcare organizations while retaining model performance. This book is aimed at graduate students and researchers in federated learning, data science, AI/machine learning, and healthcare.

Digital Infrastructure for the Learning Health System

Author : Institute of Medicine,Roundtable on Value and Science-Driven Health Care
Publisher : National Academies Press
Page : 336 pages
File Size : 53,7 Mb
Release : 2011-10-21
Category : Medical
ISBN : 9780309154161

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Digital Infrastructure for the Learning Health System by Institute of Medicine,Roundtable on Value and Science-Driven Health Care Pdf

Like many other industries, health care is increasingly turning to digital information and the use of electronic resources. The Institute of Medicine's Roundtable on Value & Science-Driven Health Care hosted three workshops to explore current efforts and opportunities to accelerate progress in improving health and health care with information technology systems.

Federated Learning and Privacy-Preserving in Healthcare AI

Author : Lilhore, Umesh Kumar,Simaiya, Sarita,Poongodi, Manoharan,Dutt, Vishal
Publisher : IGI Global
Page : 373 pages
File Size : 48,8 Mb
Release : 2024-05-02
Category : Medical
ISBN : 9798369318751

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Federated Learning and Privacy-Preserving in Healthcare AI by Lilhore, Umesh Kumar,Simaiya, Sarita,Poongodi, Manoharan,Dutt, Vishal Pdf

The use of artificial intelligence (AI) in data-driven medicine has revolutionized healthcare, presenting practitioners with unprecedented tools for diagnosis and personalized therapy. However, this progress comes with a critical concern: the security and privacy of sensitive patient data. As healthcare increasingly leans on AI, the need for robust solutions to safeguard patient information has become more pressing than ever. Federated Learning and Privacy-Preserving in Healthcare AI emerges as the definitive solution to balancing medical progress with patient data security. This carefully curated volume not only outlines the challenges of federated learning but also provides a roadmap for implementing privacy-preserving AI systems in healthcare. By decentralizing the training of AI models, federated learning mitigates the risks associated with centralizing patient data, ensuring that critical information never leaves its original location. Aimed at healthcare professionals, AI experts, policymakers, and academics, this book not only delves into the technical aspects of federated learning but also fosters a collaborative approach to address the multifaceted challenges at the intersection of healthcare and AI.

Digital Data Improvement Priorities for Continuous Learning in Health and Health Care

Author : Institute of Medicine,Roundtable on Value and Science-Driven Health Care
Publisher : National Academies Press
Page : 58 pages
File Size : 55,6 Mb
Release : 2013-04-26
Category : Medical
ISBN : 9780309259415

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Digital Data Improvement Priorities for Continuous Learning in Health and Health Care by Institute of Medicine,Roundtable on Value and Science-Driven Health Care Pdf

Digital health data are the lifeblood of a continuous learning health system. A steady flow of reliable data is necessary to coordinate and monitor patient care, analyze and improve systems of care, conduct research to develop new products and approaches, assess the effectiveness of medical interventions, and advance population health. The totality of available health data is a crucial resource that should be considered an invaluable public asset in the pursuit of better care, improved health, and lower health care costs. The ability to collect, share, and use digital health data is rapidly evolving. Increasing adoption of electronic health records (EHRs) is being driven by the implementation of the Health Information Technology for Economic and Clinical Health (HITECH) Act, which pays hospitals and individuals incentives if they can demonstrate that they use basic EHRs in 2011. Only a third had access to the basic features necessary to leverage this information for improvement, such as the ability to view laboratory results, maintain problem lists, or manage prescription ordering. In addition to increased data collection, more organizations are sharing digital health data. Data collected to meet federal reporting requirements or for administrative purposes are becoming more accessible. Efforts such as Health.Data.gov provide access to government datasets for the development of insights and software applications with the goal of improving health. Within the private sector, at least one pharmaceutical company is actively exploring release of some of its clinical trial data for research by others. Digital Data Improvement Priorities for Continuous Learning in Health and Health Care: Workshop Summary summarizes discussions at the March 2012 Institute of Medicine (2012) workshop to identify and characterize the current deficiencies in the reliability, availability, and usability of digital health data and consider strategies, priorities, and responsibilities to address such deficiencies.

Pioneering Smart Healthcare 5.0 with IoT, Federated Learning, and Cloud Security

Author : Hassan, Ahdi,Prasad, Vivek Kumar,Bhattacharya, Pronaya,Dutta, Pushan Kumar,Damaševi?ius, Robertas
Publisher : IGI Global
Page : 372 pages
File Size : 40,9 Mb
Release : 2024-02-14
Category : Medical
ISBN : 9798369326404

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Pioneering Smart Healthcare 5.0 with IoT, Federated Learning, and Cloud Security by Hassan, Ahdi,Prasad, Vivek Kumar,Bhattacharya, Pronaya,Dutta, Pushan Kumar,Damaševi?ius, Robertas Pdf

The Healthcare sector is experiencing a mindset change with the advent of Healthcare 5.0, bringing forth improved patient care and system efficiency. However, this transformation poses significant challenges. The growing digitization of healthcare systems raises concerns about the security and privacy of patient data, making seamless data sharing and collaboration increasingly complex tasks. Additionally, as the volume of healthcare data expands exponentially, efficient handling and analysis become vital for optimizing healthcare delivery and patient outcomes. Addressing these multifaceted issues is crucial for healthcare professionals, IT experts, data scientists, and researchers seeking to fully harness the potential of Healthcare 5.0. Pioneering Smart Healthcare 5.0 with IoT, Federated Learning, and Cloud Security presents a comprehensive solution to the pressing challenges in the digitalized healthcare industry. This research book dives into the principles of Healthcare 5.0 and explores practical implementation through cloud computing, data analytics, and federated learning. Readers will gain profound insights into the role of cloud computing in managing vast amounts of healthcare data, such as electronic health records and real-time analytics. Cloud-based frameworks, architectures, and relevant use cases are explored to optimize healthcare delivery and improve patient outcomes.

Artificial Intelligence in Healthcare

Author : Adam Bohr,Kaveh Memarzadeh
Publisher : Academic Press
Page : 385 pages
File Size : 52,5 Mb
Release : 2020-06-21
Category : Computers
ISBN : 9780128184394

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Artificial Intelligence in Healthcare by Adam Bohr,Kaveh Memarzadeh Pdf

Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. Highlights different data techniques in healthcare data analysis, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks Includes applications and case studies across all areas of AI in healthcare data

Accelerating Strategic Changes for Digital Transformation in the Healthcare Industry

Author : Patricia Ordonez de Pablos,Xi Zhang
Publisher : Elsevier
Page : 426 pages
File Size : 53,8 Mb
Release : 2023-05-30
Category : Computers
ISBN : 9780443153006

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Accelerating Strategic Changes for Digital Transformation in the Healthcare Industry by Patricia Ordonez de Pablos,Xi Zhang Pdf

Accelerating Strategic Changes for Digital Transformation in the Healthcare Industry discusses innovative conceptual frameworks, tools and solutions to tackle the challenges of mitigating major disruption caused by COVID-19 in the healthcare sector and society. It emphasizes global case studies and empirical studies, providing a comprehensive view of best lessons on digital tools to manage the health crisis. The book focuses on the role of advances in digital and collaborative technologies to offer rapid and effective tools for better health solutions for new and emerging health problems. Researchers, students, policymakers and members of the biomedical and medical fields will find this information invaluable. Specially, it pays attention to how information technologies help us in the current global health emergency and the coronavirus epidemic response, gaining more understanding of the new coronavirus and helping to contain the outbreak. In addition, it explores how these new tools and digital health solutions can support the economic and social recovery in the post-pandemic world. Discusses best experiences, tools and solutions provided by IT to solve the global disruption caused by the COVID-19 pandemic in societies, healthcare infrastructures and health workers Presents case studies with experiences of applications of digital healthcare solutions from around the world Encompasses the point of views of renown researchers and academics globally that are working collaboratively to explore new views and frameworks to develop solutions for emergent problems in the healthcare sector

Digital Health Care: Perspectives, Applications, and Cases

Author : Phillip Olla,Joseph Tan
Publisher : Jones & Bartlett Learning
Page : 304 pages
File Size : 50,5 Mb
Release : 2022-05-04
Category : Medical
ISBN : 9781284254662

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Digital Health Care: Perspectives, Applications, and Cases by Phillip Olla,Joseph Tan Pdf

Digital Health Care: Perspectives, Applications, and Cases explores the trends, perspectives, and cases of Digital Healthcare and Informatics (DHI) that are transforming healthcare across the globe. Organized in 5 major connecting parts, this well-conceived text begins by laying out foundational DHI themes before focusing in on key DHI core technologies, developments, methods and challenges -- from big data analytics & artificial intelligence to security and privacy issues, clinical decision support systems, consumer health informatics, and more. It then explores DHI emerging technologies (e.g. sensors and wearable electronics), and concludes with short case studies and critical case questions designed to reinforce conceptual understanding. Written for undergraduates health professionals, this accessible text offers a multidisciplinary perspective that is suitable for use in variety of healthcare disciplines - from allied health and nursing to health administration & public health