The writing of this text arose through the opportunity of one author (D.F.) to spend a sabbatical leave studying the developments within the Stockholm County health operations. The computerization project that had started virtually 10 years previously. focussed on the Danderyd hospital. had received continuing attention in the world scene. It therefore seemed an appropriate site for study for one charged with responsibility for developing computerized hospital information systems. An intent to assemble a private report became an attempt to write a public monograph when it was discovered that the leaders of the Stockholm project had aspired for some time to put their work more cohesively in the public domain. Since any such publication would be in English and oriented to an international market. the involvement of one extraneous author representative of that market obviously had its appropriateness - in language. interest and detachment. The resulting book is unusual. if not unique. because of its lengthy and detailed description of one system * Starting from an introduction to Sweden and the local health care system. it proceeds through to detailed descriptions of user procedures. terminal displays. and computer files. Undoubtedly some readers will question the merit in having such detail. particularly as it relates to outmoded equipment and a locally developed programming language. Yet some of those same readers might well be among those many people who ask elsewhere why we should repeatedly "re-invent the wheel" in the computer industry.

Technologies such as artificial intelligence have led to significant advances in science and medicine, but have also facilitated new forms of repression, policing and surveillance. AI policy has become without doubt a significant issue of global politics. The Global Politics of Artificial Intelligence tackles some of the issues linked to AI development and use, contributing to a better understanding of the global politics of AI. This is an area where enormous work still needs to be done, and the contributors to this volume provide significant input into this field of study, to policy makers, academics, and society at large. Each of the chapters in this volume works as freestanding contribution, and provides an accessible account of a particular issue linked to AI from a political perspective. Contributors to the volume come from many different areas of expertise, and of the world, and range from emergent to established authors.

Becoming a Digital Parent is a practical, readable guide that will help all parents have confidence to successfully navigate technology with their children. It accessibly presents evidence-based guidance to offer an overview of the digital landscape, empowering parents to embrace opportunities whilst keeping children responsible and safe online. Covering a range of topics including developmental stages, screen time, bed time, gaming, digital identities, and helpful parenting apps and resources, Carrie Rogers-Whitehead explores the challenges and opportunities involved in parenting in the digital age. With advice for parents of babies through to teenagers, each chapter includes an explanation of the latest research, interviews with parents and experts, and helpful case studies gathered by the author during her extensive experience of working directly with parents and children. This book will show parents how to communicate better with their children, create a family technology plan, put in place intervention strategies when things happen, and take advantage of the benefits technology can afford us. Becoming a Digital Parent is ideal for all parents looking to effectively navigate the technological world, and the range of professionals who work with them.

Artificial intelligence (AI) and machine learning (ML) have transformed many standard and conventional methods in undertaking health and well-being issues of humans. AL/ML-based systems and tools play a critical role in this digital and big data era to address a variety of medical and healthcare problems, improving treatments and quality of care for patients. This edition on AI and ML for healthcare consists of two volumes. The first presents selected AI and ML studies on medical imaging and healthcare data analytics, while the second unveils emerging methodologies and trends in AI and ML for delivering better medical treatments and healthcare services in the future. In this first volume, progresses in AI and ML technologies for medical image, video, and signal processing as well as health information and data analytics are presented. These selected studies offer readers theoretical and practical knowledge and ideas pertaining to recent advances in AI and ML for effective and efficient image and data analytics, leading to state-of-the-art AI and ML technologies for advancing the healthcare sector.

This book gathers peer-review contributions to the 4th International Workshop on Gerontechnology, IWoG 2021, held on November 23-24, 2021, in Evora, Portugal. They report on cutting-edge technologies and optimized workflows for promoting active aging and assisting elderly people at home, as well as in healthcare centers. They discuss the main challenges in the development, use and delivery of health care services and technologies. Not only they propose solutions for improving in practice the monitoring and management of health parameters and age-related diseases, yet they also describe improved approaches for helping seniors in their daily tasks and facilitating their communication and integration with assistive technologies, thus improving their quality of life, as well as their social integration. All in all, this book provides health professionals, researchers, and service providers with extensive information on the latest trends in the development and practical application of gerontechnology, with a special emphasis on improving quality of life of the elderly.

This book originates from the idea to adapt biomedical engineering and medical informatics to current clinical needs and proposes a paradigm shift in medical engineering, where the limitations of technology should no longer be the starting point of design, but rather the development of biomedical devices, software, and systems should stem from clinical needs and wishes. Gathering chapters written by authoritative researchers, working the interface between medicine and engineering, this book presents successful attempts of conceiving technology based on clinical practice. It reports on new strategies for medical diagnosis, rehabilitation, and eHealth, focusing on solutions to foster better quality of life through technology, with an emphasis on patients' and clinical needs, and vulnerable populations. All in all, the book offers a reference guide and a source of inspiration for biomedical engineers, clinical scientists, physicians, and computer scientists. Yet, it also includes practical information for personnel using biomedical equipment, as well as timely insights that are expected to help health agencies and software firms in their decision-making processes.

This textbook presents deep learning models and their healthcare applications. It focuses on rich health data and deep learning models that can effectively model health data. Healthcare data: Among all healthcare technologies, electronic health records (EHRs) had vast adoption and a significant impact on healthcare delivery in recent years. One crucial benefit of EHRs is to capture all the patient encounters with rich multi-modality data. Healthcare data include both structured and unstructured information. Structured data include various medical codes for diagnoses and procedures, lab results, and medication information. Unstructured data contain 1) clinical notes as text, 2) medical imaging data such as X-rays, echocardiogram, and magnetic resonance imaging (MRI), and 3) time-series data such as the electrocardiogram (ECG) and electroencephalogram (EEG). Beyond the data collected during clinical visits, patient self-generated/reported data start to grow thanks to wearable sensors' increasing use. The authors present deep learning case studies on all data described. Deep learning models: Neural network models are a class of machine learning methods with a long history. Deep learning models are neural networks of many layers, which can extract multiple levels of features from raw data. Deep learning applied to healthcare is a natural and promising direction with many initial successes. The authors cover deep neural networks, convolutional neural networks, recurrent neural networks, embedding methods, autoencoders, attention models, graph neural networks, memory networks, and generative models. It's presented with concrete healthcare case studies such as clinical predictive modeling, readmission prediction, phenotyping, x-ray classification, ECG diagnosis, sleep monitoring, automatic diagnosis coding from clinical notes, automatic deidentification, medication recommendation, drug discovery (drug property prediction and molecule generation), and clinical trial matching. This textbook targets graduate-level students focused on deep learning methods and their healthcare applications. It can be used for the concepts of deep learning and its applications as well. Researchers working in this field will also find this book to be extremely useful and valuable for their research.

This book focuses on recent advances and different research areas in multi-modal data fusion under healthcare informatics and seeks out theoretical, methodological, well-established and validated empirical work dealing with these different topics. This book brings together the latest industrial and academic progress, research, and development efforts within the rapidly maturing health informatics ecosystem. Contributions highlight emerging data fusion topics that support prospective healthcare applications. The book also presents various technologies and concerns regarding energy aware and secure sensors and how they can reduce energy consumption in health care applications. It also discusses the life cycle of sensor devices and protocols with the help of energy-aware design, production, and utilization, as well as the Internet of Things technologies such as tags, sensors, sensing networks, and Internet technologies. In a nutshell, this book gives a comprehensive overview of the state-of-the-art theories and techniques for massive data handling and access in medical data and smart health in IoT, and provides useful guidelines for the design of massive Internet of Medical Things.

This book features selected papers from the International Conference on Communication and Applied Technologies (ICOMTA 2021), jointly organized by Universidad del Rosario (Bogota, Colombia); the University of Vigo (Galicia, Spain); the University of Santiago de Compostela-Equipo de Investigaciones Politicas (Galicia, Spain); the University of A Coruna (Galicia, Spain); and the Information and Technology Management Association (ITMA), during September 2021. It covers recent advances in the field of digital communication and processes digital social media, software, big data, data mining, and intelligent systems.

Teleradiologische Befundung wird immer wichtiger fur Kliniken, die sich nachts und Feiertags keinen Radiologen leisten koennen oder wollen. Mit der immer populareren Telemedizin ist auch die Teleradiologie seit Jahren immer mehr gewachsen. Ziel des Buches ist es, umfassend uber das teleradiologische Arbeiten, die Moeglichkeiten, Arbeitsablaufe, Infrastruktur und Anwendungsbereiche zu informieren. Dabei wird sowohl die Anwenderseite (der radiologische Arzt als Befunder) als auch die Auftraggeber-Seite (die Kliniken) beleuchtet, die hier eng verzahnt interagieren mussen. Inhalte: Das Buch informiert den Leser umfassend uber die folgenden Aspekten der Teleradiologie AErztliche Aspekte Sicht des Krankenhaus-Managements, gesetzliche Vorgaben (Strahlenschutzgesetz und -verordnung), DIN-Normen, Arbeitsablaufe, Genehmigungsprozess, Qualitatsaspekte, Integration in die IT-Landschaft der Beteiligten, Kommunikationsstandards in der Medizin/Radiologie, Beispiele realisierter Teleradiologie-Netzwerke, Datenschutz und Rechtsfragen .

In line with advances in digital and computing systems, artificial intelligence (AI) and machine learning (ML) technologies have transformed many aspects of medical and healthcare services, delivering tangible benefits to patents and the general public. This book is a sequel of the edition on "Artificial Intelligence and Machine Learning for Healthcare". The first volume is focused on utilization of AI and ML for image and data analytics in the medical and healthcare domains. In this second volume, emerging methodologies and future trends in AI and ML for advancing medical treatments and healthcare services are presented. The selected studies in this book provide readers a glimpse on current progresses in AI and ML for undertaking a variety of healthcare-related tasks. The advances in AI and ML technologies for future healthcare are also discussed, shedding light on the potential of AI and ML to realize the next-generation medical treatments and healthcare services for the betterment of our global society.

This book presents work on healthcare management and engineering using optimization and simulation methods and techniques. Specific topics covered in the contributed chapters include discrete-event simulation, patient admission scheduling, simulation-based emergency department control systems, patient transportation, cost function networks, hospital bed management, and operating theater scheduling. The content will be valuable for researchers and postgraduate students in computer science, information technology, industrial engineering, and applied mathematics.

This book aims to facilitate and improve development work related to all documents and information required by functional safety standards. Proof of Compliance (PoC) is important for the assessor and certification bodies when called up to confirm that the manufacturer has developed a software system according to the required safety standards. While PoC documents add functionality to the product neither for the developer nor for the customer, they do add confidence and trust to the product and ease certification, and as such are important for the product's value. In spite of this added value, the documentation needed for PoC is often developed late in the project and in a haphazard manner. This book aims at developers, assessors, certification bodies, and purchasers of safety instrumented systems and informs the reader about the most important PoC documents. A typical PoC documentation encompasses 50 to 200 documents, several of which are named in the safety standards (e.g., 82 documents in IEC 61508:2010 series, 101 documents in EN 5012X series and 106 work products in ISO 26262:2018 series). These documents also include further references, typically one to twenty of them, and the total number of pages developed by the manufacturer varies between 2000 and 10000 pages. The book provides guidance and examples what to include in the relevant plans and documents.

This book constitutes refereed proceedings of the 8th China Conference on China Health Information Processing Conference 2022 held in Hangzhou, China from August 26-28, 2022. The 14 full papers presented in this volume were carefully reviewed and selected from a total of 35 submissions. The papers in the volume are organised according to the following topical headings: healthcare natural language processing;healthcare data mining and applications

Heavily updated and revised from the successful first edition Appeals to a wide range of informatics professionals, from students to on-site medical information system administrators Includes case studies and real world system evaluations References and self-tests for feedback and motivation after each chapter Great for teaching purposes, the book is recommended for courses offered at universities such as Columbia University Precise definition and use of terms

This book constitutes the proceedings of the 11th Workshop on Clinical Image-Based Procedures, CLIP 2022, which was held in conjunction with MICCAI 2022, in Singapore in September 2022. The 9 full papers included in this book were carefully reviewed and selected from 12 submissions. They focus on the applicability of basic research methods in the clinical practice by creating holistic patient models as an important step towards personalized healthcare.

This book is a proficient guide to understanding artificial intelligence (IoT) and the Internet of Medical Things (IoMT) in healthcare. The book provides a comprehensive study on the applications of AI and IoT in various medical domains. The book shows how the implementation of innovative solutions in healthcare is beneficial, and IoT, together with AI, are strong drivers of the digital transformation regardless of what field the technologies are applied in. Therefore, this book provides a high level of understanding with the emerging technologies on the Internet of Things, wearable devices, and AI in IoMT, which offers the potential to acquire and process a tremendous amount of data from the physical world.

Artificial Intelligence (AI) has transformed many aspects of our daily activities. Health and well-being of humans stand as one of the key domains where AI has achieved significant progresses, saving time, costs, and potentially lives, as well as fostering economic resilience, particularly under the COVID-19 pandemic environments. This book is a sequel of the Handbook of Artificial Intelligence in Healthcare. The first volume of the Handbook is dedicated to present advances and applications of AI methodologies in several specific areas, i.e., signal, image, and video processing as well as information and data analytics. In this second volume of the Handbook, general practicality challenges and future prospects of AI methodologies pertaining to healthcare and related domains are presented in Part 1 and Part 2, respectively. It is envisaged that the selected studies will provide readers a general perspective on the issues, challenges, and opportunities in designing, developing, and implementing AI-based tools and solutions in the healthcare sector, bringing benefits to transform and advance health and well-being development of humans..

This textbook presents deep learning models and their healthcare applications. It focuses on rich health data and deep learning models that can effectively model health data. Healthcare data: Among all healthcare technologies, electronic health records (EHRs) had vast adoption and a significant impact on healthcare delivery in recent years. One crucial benefit of EHRs is to capture all the patient encounters with rich multi-modality data. Healthcare data include both structured and unstructured information. Structured data include various medical codes for diagnoses and procedures, lab results, and medication information. Unstructured data contain 1) clinical notes as text, 2) medical imaging data such as X-rays, echocardiogram, and magnetic resonance imaging (MRI), and 3) time-series data such as the electrocardiogram (ECG) and electroencephalogram (EEG). Beyond the data collected during clinical visits, patient self-generated/reported data start to grow thanks to wearable sensors' increasing use. The authors present deep learning case studies on all data described. Deep learning models: Neural network models are a class of machine learning methods with a long history. Deep learning models are neural networks of many layers, which can extract multiple levels of features from raw data. Deep learning applied to healthcare is a natural and promising direction with many initial successes. The authors cover deep neural networks, convolutional neural networks, recurrent neural networks, embedding methods, autoencoders, attention models, graph neural networks, memory networks, and generative models. It's presented with concrete healthcare case studies such as clinical predictive modeling, readmission prediction, phenotyping, x-ray classification, ECG diagnosis, sleep monitoring, automatic diagnosis coding from clinical notes, automatic deidentification, medication recommendation, drug discovery (drug property prediction and molecule generation), and clinical trial matching. This textbook targets graduate-level students focused on deep learning methods and their healthcare applications. It can be used for the concepts of deep learning and its applications as well. Researchers working in this field will also find this book to be extremely useful and valuable for their research.

This book emphasizes the latest developments and achievements in artificial intelligence and related technologies, focusing on the applications of artificial intelligence and medical diagnosis. The book describes the theory, applications, concept visualization, and critical surveys covering most aspects of AI for medical informatics.

This book originates from the idea to adapt biomedical engineering and medical informatics to current clinical needs and proposes a paradigm shift in medical engineering, where the limitations of technology should no longer be the starting point of design, but rather the development of biomedical devices, software, and systems should stem from clinical needs and wishes. Gathering chapters written by authoritative researchers, working the interface between medicine and engineering, this book presents successful attempts of conceiving technology based on clinical practice. It reports on new strategies for medical diagnosis, rehabilitation, and eHealth, focusing on solutions to foster better quality of life through technology, with an emphasis on patients' and clinical needs, and vulnerable populations. All in all, the book offers a reference guide and a source of inspiration for biomedical engineers, clinical scientists, physicians, and computer scientists. Yet, it also includes practical information for personnel using biomedical equipment, as well as timely insights that are expected to help health agencies and software firms in their decision-making processes.

This book constitutes the refereed proceedings of the 11th International Conference on Health Information Science, HIS 2022, held in Virtual Event during October 28-30, 2022. The 20 full papers and 9 short papers included in this book were carefully reviewed andselected from 54 submissions. They were organized in topical sections as follows: applications of health and medical data; health and medical data processing; health and medical data mining via graph-based approaches; and health and medical data classification.

This edited book helps researchers and practitioners to understand e-health, m-healthcare architecture through IoT and the state of the art in IoT counter measures. This book provides a comprehensive discussion on a functional framework for IoT-based healthcare systems, intelligent medicine box, RFID technology, HMI, cognitive interpretation, BCI, remote health monitoring systems, wearable sensors, WBAN, healthcare analytics, machine learning (ML) techniques for IoT-enabled healthcare services, security and privacy issues in IoT-based healthcare monitoring systems. The book discusses integration of IoT with big data and cloud computing for solving several real-time problems by the use of smart healthcare applications. In these applications, the cloud computing provides a common workplace for IoT and big data, big data provides data analytics technology and IoT provides the source of data. It serves as a reference resource for researchers and practitioners in academia and industry.

This open access book presents a set of practical tools and collaborative solutions in multi-disciplinary settings to foster the Alpine Space health tourism industry's innovation and competitiveness. The proposed solutions emerge as the result of the synergy among health, environment, tourism, digital, policy and strategy professionals. The approach underlines the pivotal role of a sustainable and ecomedical use of Alpine natural resources for health tourism destinations, and highlights the need of integrating aspects of natural resources' healing effects, a shared knowledge of Alpine assets through digital solutions, and frames strategic approaches for the long-term development of the sector. The volume exploits the results of the three-years long EU research project HEALPS 2, which involved several stakeholders from the health tourism, healthcare and sustainable tourism industries. This book is relevant for health tourism destinations and facilities (hotels, clinics, wellness and spa companies), regional and local authorities (policy makers), business support organizations, researchers involved in digital healthcare and geoinformatics.

This handbook on Artificial Intelligence (AI) in healthcare consists of two volumes. The first volume is dedicated to advances and applications of AI methodologies in specific healthcare problems, while the second volume is concerned with general practicality issues and challenges and future prospects in the healthcare context. The advent of digital and computing technologies has created a surge in the development of AI methodologies and their penetration to a variety of activities in our daily lives in recent years. Indeed, researchers and practitioners have designed and developed a variety of AI-based systems to help advance health and well-being of humans. In this first volume, we present a number of latest studies in AI-based tools and techniques from two broad categories, viz., medical signal, image, and video processing as well as healthcare information and data analytics in Part 1 and Part 2, respectively. These selected studies offer readers practical knowledge and understanding pertaining to the recent advances and applications of AI in the healthcare sector.