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SEMANTIC WEB FOR EFFECTIVE HEALTHCARE SYSTEMS The book summarizes the trends and current research advances in web semantics, delineating the existing tools, techniques, methodologies, and research solutions Semantic Web technologies have the opportunity to transform the way healthcare providers utilize technology to gain insights and knowledge from their data and make treatment decisions. Both Big Data and Semantic Web technologies can complement each other to address the challenges and add intelligence to healthcare management systems. The aim of this book is to analyze the current status on how the semantic web is used to solve health data integration and interoperability problems, and how it provides advanced data linking capabilities that can improve search and retrieval of medical data. Chapters analyze the tools and approaches to semantic health data analysis and knowledge discovery. The book discusses the role of semantic technologies in extracting and transforming healthcare data before storing it in repositories. It also discusses different approaches for integrating heterogeneous healthcare data. This innovative book offers: The first of its kind and highlights only the ontology driven information retrieval mechanisms and techniques being applied to healthcare as well as clinical information systems; Presents a comprehensive examination of the emerging research in areas of the semantic web; Discusses studies on new research areas including ontological engineering, semantic annotation and semantic sentiment analysis; Helps readers understand key concepts in semantic web applications for the biomedical engineering and healthcare fields; Includes coverage of key application areas of the semantic web. Audience: Researchers and graduate students in computer science, biomedical engineering, electronic and software engineering, as well as industry scientific researchers, clinicians, and systems managers in biomedical fields.
Leveraging Biomedical and Healthcare Data: Semantics, Analytics and Knowledge provides an overview of the approaches used in semantic systems biology, introduces novel areas of its application, and describes step-wise protocols for transforming heterogeneous data into useful knowledge that can influence healthcare and biomedical research. Given the astronomical increase in the number of published reports, papers, and datasets over the last few decades, the ability to curate this data has become a new field of biomedical and healthcare research. This book discusses big data text-based mining to better understand the molecular architecture of diseases and to guide health care decision. It will be a valuable resource for bioinformaticians and members of several areas of the biomedical field who are interested in understanding more about how to process and apply great amounts of data to improve their research.
Web Semantics strengthen the description of web resources to exploit them better and make them more meaningful for both humans and machines, thereby contributing to the development of a knowledgeintensive data web. The world is experiencing the movement of concept from data to knowledge and the movement of web from document model to data model. The underlying idea is making the data machine understandable and processable. In the light of these trends, conciliation of Semantic and the Web is of paramount importance for further progress in the area. Web Semantics: Cutting Edge and Future Directions in Healthcare describes the three major components of the study of Semantic Web, namely Representation, Reasoning, and Security with a special focus on the healthcare domain. This book summarizes the trends and current research advances in web semantics, emphasizing the existing tools and techniques, methodologies, and research solutions. It provides easily comprehensible information on Web Semantics including semantics for data and semantics for services. Presents a comprehensive examination of the emerging research in areas of the semantic web, including ontological engineering, semantic annotation, reasoning and intelligent processing, semantic search paradigms, semantic web mining, and semantic sentiment analysis Helps readers understand key concepts in semantic web applications for biomedical engineering and healthcare, including mapping disparate knowledge bases, security issues, multilingual semantic web, and integrating databases with knowledge bases Includes coverage of key application areas of the semantic web, including clinical decision-making, biodiversity science, interactive healthcare, intelligent agent systems, decision support systems, and clinical natural language processing
Semantic Models in IoT and eHealth Applications explores the key role of semantic web modeling in eHealth technologies, including remote monitoring, mobile health, cloud data and biomedical ontologies. The book explores different challenges and issues through the lens of various case studies of healthcare systems currently adopting these technologies. Chapters introduce the concepts of semantic interoperability within a healthcare model setting and explore how semantic representation is key to classifying, analyzing and understanding the massive amounts of biomedical data being generated by connected medical devices. Continuous health monitoring is a strong solution which can provide eHealth services to a community through the use of IoT-based devices that collect sensor data for efficient health diagnosis, monitoring and treatment. All of this collected data needs to be represented in the form of ontologies which are considered the cornerstone of the Semantic Web for knowledge sharing, information integration and information extraction. - Presents comprehensive coverage of advances in the application of semantic web in the field of eHealth - Explores different challenges and issues through various case studies of healthcare systems that are adopting semantic web technologies - Covers applications across a range of eHealth technologies, including remote monitoring and mobile health
Data Democracy: At the Nexus of Artificial Intelligence, Software Development, and Knowledge Engineering provides a manifesto to data democracy. After reading the chapters of this book, you are informed and suitably warned! You are already part of the data republic, and you (and all of us) need to ensure that our data fall in the right hands. Everything you click, buy, swipe, try, sell, drive, or fly is a data point. But who owns the data? At this point, not you! You do not even have access to most of it. The next best empire of our planet is one who owns and controls the world’s best dataset. If you consume or create data, if you are a citizen of the data republic (willingly or grudgingly), and if you are interested in making a decision or finding the truth through data-driven analysis, this book is for you. A group of experts, academics, data science researchers, and industry practitioners gathered to write this manifesto about data democracy. The future of the data republic, life within a data democracy, and our digital freedoms An in-depth analysis of open science, open data, open source software, and their future challenges A comprehensive review of data democracy's implications within domains such as: healthcare, space exploration, earth sciences, business, and psychology The democratization of Artificial Intelligence (AI), and data issues such as: Bias, imbalance, context, and knowledge extraction A systematic review of AI methods applied to software engineering problems
This book constitutes the refereed proceedings of the 16th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2022, which took place in Thessaloniki, Greece, in December 2022. The 45 full papers included in this volume were carefully reviewed and selected from 120 submissions. The papers are organized in the following topical sections: personal informatics and wearable devices; computer vision; IoT-HR: Internet of things in health research; pervasive health for COVID-19; machine learning, human activity recognition and speech recognition; software frameworks and interoperability; facial recognition, gesture recognition and object detection; machine learning, predictive models and personalised healthcare; human-centred design of pervasive health solutions; personalized healthcare.
This 3rd edition of a classic textbook examines the context and background of public health informatics, explores the technology and science underlying the field, discusses challenges and emerging solutions, reviews many key public health information systems, and includes practical, case-based studies to guide the reader through the topic. The editors have expanded the text into new areas that have become important since publication of the previous two editions due to changing technologies and needs in the field, as well as updating and augmenting much of the core content. The book contains learning objectives, overviews, future directions, and review questions to assist readers to engage with this vast topic. The Editors and their team of well-known contributors have built upon the foundation established by the previous editions to provide the reader with a comprehensive and forward-looking review of public health informatics. The breadth of material in Public Health Informatics and Information Systems, 3rd edition makes it suitable for both undergraduate and graduate coursework in public health informatics, enabling instructors to select chapters that best fit their students’ needs.
The data that must be processed in healthcare includes text, numbers, statistics, and images, and healthcare systems are continuously acquiring novel data from cutting-edge technologies like wearable devices. Semantic intelligence technologies, such as artificial intelligence, machine learning, and the internet of things, together with the hybrid methodologies which combine these approaches, are central to the development of the intelligent, knowledge-based systems now used in healthcare. This book, Roles and Challenges of Semantic Intelligence in Healthcare Cognitive Computing explores those emerging fields of science and technology in which cognitive computing techniques offer the effective solutions poised to impact healthcare in the foreseeable future, minimizing errors and improving the effectiveness of personalized care models. The book assesses the current landscape, and identifies the roles and challenges of integrating cognitive computing techniques into the widespread adoption of innovative smart healthcare solutions. Each chapter is the result of collaboration by experts from various domains, and provides a detailed overview of the potential offered by new technologies in the field. A wide spectrum of topics and emerging trends are covered, reflecting the multidisciplinary nature of healthcare and cognitive computing and including digital twins, eXplainable AI, AI-based decision-support systems in intensive care, and culinary healthcare, as well as the semantic internet of things (SIoT), natural language processing, and deep learning and graph models. The book presents new ideas which will facilitate collaboration among the different disciplines involved, and will be of interest to all those working in this rapidly evolving field.