This book presents the proceedings of the KES International Conferences on Innovation in Medicine and Healthcare (KES-InMed-19), held in Split, Croatia, on June 17-19, 2020. Covering a number of key areas, including digital IT architecture in healthcare; advanced ICT for medicine and healthcare; biomedical engineering, trends, research and technologies; and healthcare support systems, this book is a valuable resource for researchers, managers, industrialists and anyone wishing to gain an overview of the latest research in these fields.

eHealth Applications: Promising Strategies for Behavior Change provides an overview of technological applications in contemporary health communication research, exploring the history and current uses of eHealth applications in disease prevention and management. This volume focuses on the use of these technology-based interventions for public health promotion and explores the rapid growth of an innovative interdisciplinary field.

The chapters in this work discuss key eHealth applications by presenting research examining a variety of technology-based applications. Authors Seth M. Noar and Nancy Grant Harrington summarize the latest in eHealth research, including a range of computer, Internet, and mobile applications, and offer observations and reflections on this growing area, such as dissemination of programs and future directions for the study of interactive health communication and eHealth.

Providing a timely and comprehensive review of current tools for health communication, eHealth Applications is a must-read for scholars, students, and researchers in health communication, public health, and health education.

This book explores potentially disruptive and transformative healthcare-specific use cases made possible by the latest developments in Internet of Things (IoT) technology and Cyber-Physical Systems (CPS). Healthcare data can be subjected to a range of different investigations in order to extract highly useful and usable intelligence for the automation of traditionally manual tasks. In addition, next-generation healthcare applications can be enhanced by integrating the latest knowledge discovery and dissemination tools. These sophisticated, smart healthcare applications are possible thanks to a growing ecosystem of healthcare sensors and actuators, new ad hoc and application-specific sensor and actuator networks, and advances in data capture, processing, storage, and mining. Such applications also take advantage of state-of-the-art machine and deep learning algorithms, major strides in artificial and ambient intelligence, and rapid improvements in the stability and maturity of mobile, social, and edge computing models.

This book provides step-by-step explanations of successful implementations and practical applications of machine learning. The book's GitHub page contains software codes to assist readers in adapting materials and methods for their own use. A wide variety of applications are discussed, including wireless mesh network and power systems optimization; computer vision; image and facial recognition; protein prediction; data mining; and data discovery. Numerous state-of-the-art machine learning techniques are employed (with detailed explanations), including biologically-inspired optimization (genetic and other evolutionary algorithms, swarm intelligence); Viola Jones face detection; Gaussian mixture modeling; support vector machines; deep convolutional neural networks with performance enhancement techniques (including network design, learning rate optimization, data augmentation, transfer learning); spiking neural networks and timing dependent plasticity; frequent itemset mining; binary classification; and dynamic programming. This book provides valuable information on effective, cutting-edge techniques, and approaches for students, researchers, practitioners, and teachers in the field of machine learning.

This book covers emerging trends in signal processing research and biomedical engineering, exploring the ways in which signal processing plays a vital role in applications ranging from medical electronics to data mining of electronic medical records. Topics covered include statistical modeling of electroencephalograph data for predicting or detecting seizure, stroke, or Parkinson's; machine learning methods and their application to biomedical problems, which is often poorly understood, even within the scientific community; signal analysis; medical imaging; and machine learning, data mining, and classification. The book features tutorials and examples of successful applications that will appeal to a wide range of professionals and researchers interested in applications of signal processing, medicine, and biology.

Moritz Goeldner analyzes the unexplored phenomenon of patients and caregivers as innovators with respect to their own unmet medical needs in two complementary studies. In study 1 he uses a mixed-method approach to analyze quantitative data from two datasets on more than 1,100 medical smartphone apps each and qualitative data from 16 interviews with developers of medical apps. He finds substantial evidence that patients and caregivers develop medical apps and shows that those apps receive significantly better ratings than company-developed apps. In study 2 he further explores the commercialization activities of patients and caregivers by analyzing 14 case studies of patients and caregivers who successfully brought their tangible medical device on the market. He finds that those innovators did not maximize their profits, but rather sought to market their devices at reasonable prices to offer access to many other patients. The author discusses these insights and draws conclusions for scholars and managers that are valid beyond this extreme case of user innovation. About the author Moritz Goeldner is an innovation consultant for user-centered innovation in (digital) healthcare. Prior to this position, he was a project manager and research associate at the Institute for Technology and Innovation Management at Hamburg University of Technology. His research interests cover user innovation in healthcare, social innovation, the emergence of new medical technologies, as well as entrepreneurship.

This book addresses various aspects of how smart healthcare can be used to detect and analyze diseases, the underlying methodologies, and related security concerns. Healthcare is a multidisciplinary field that involves a range of factors like the financial system, social factors, health technologies, and organizational structures that affect the healthcare provided to individuals, families, institutions, organizations, and populations. The goals of healthcare services include patient safety, timeliness, effectiveness, efficiency, and equity. Smart healthcare consists of m-health, e-health, electronic resource management, smart and intelligent home services, and medical devices. The Internet of Things (IoT) is a system comprising real-world things that interact and communicate with each other via networking technologies. The wide range of potential applications of IoT includes healthcare services. IoT-enabled healthcare technologies are suitable for remote health monitoring, including rehabilitation, assisted ambient living, etc. In turn, healthcare analytics can be applied to the data gathered from different areas to improve healthcare at minimum expense.

This book presents papers from HealthyIoT 2018, the fifth edition of an international scientific event series dedicated to Internet of Things and Healthcare. The papers discuss leveraging a set of existing and emerging technologies, notions and services that can provide many solutions to delivery of electronic healthcare, patient care, and medical data management. HealthyIoT brings together technology experts, researchers, industry and international authorities contributing towards the design, development and deployment of healthcare solutions based on IoT technologies, standards, and procedures. HealthyIoT 2018 is part of the 4th annual Smart City 360 Summit, promoting multidisciplinary scientific collaboration to solve complex societal, technological and economic problems of emerging Smart Cities. The event is endorsed by the European Alliance for Innovation, an international professional community-based organisation devoted to the advancement of innovation in the field of ICT. Features practical, tested applications in IoT for healthcare; Includes application domains such as eHealth Systems, smart textiles, smart caring environments, telemedicine, wellness, and health management, etc; Applicable to researchers, academics, students, and professionals.

This book reports on the theoretical foundations, fundamental applications and latest advances in various aspects of connected services for health information systems. The twelve chapters highlight state-of-the-art approaches, methodologies and systems for the design, development, deployment and innovative use of multisensory systems and tools for health management in smart city ecosystems. They exploit technologies like deep learning, artificial intelligence, augmented and virtual reality, cyber physical systems and sensor networks. Presenting the latest developments, identifying remaining challenges, and outlining future research directions for sensing, computing, communications and security aspects of connected health systems, the book will mainly appeal to academic and industrial researchers in the areas of health information systems, smart cities, and augmented reality.

This book applies novel theories to improve algorithms in complex data analysis in various fields, including object detection, remote sensing, data transmission, data fusion, gesture recognition, and medical image processing and analysis. It is intended for Ph.D. students, academics, researchers, and software developers working in the areas of digital video processing and computer vision technologies.

This book is written in a very easy-to-follow format, and explains the key concepts of biomedical statistics in a lucid yet straightforward manner. It explains how mathematical and statistical tools can be used to find answers to common research questions. In addition, the main text is supplemented by a wealth of solved exercises and illustrative examples to aid in comprehension. Given its content, the book offers an invaluable quick reference guide for graduating students and can be very helpful in their examination process. At the same time, it represents a handy guide for medical and paramedical teachers, post-graduate medical students, research personnel, biomedical scientists and epidemiologists.

This in-depth book addresses a key void in the literature surrounding the Internet of Things (IoT) and health. By systematically evaluating the benefits of mobile, wireless, and sensor-based IoT technologies when used in health and wellness contexts, the book sheds light on the next frontier for healthcare delivery. These technologies generate data with significant potential to enable superior care delivery, self-empowerment, and wellness management. Collecting valuable insights and recommendations in one accessible volume, chapter authors identify key areas in health and wellness where IoT can be used, highlighting the benefits, barriers, and facilitators of these technologies as well as suggesting areas for improvement in current policy and regulations. Four overarching themes provide a suitable setting to examine the critical insights presented in the 31 chapters: Mobile- and sensor-based solutions Opportunities to incorporate critical aspects of analytics to provide superior insights and thus support better decision-making Critical issues around aspects of IoT in healthcare contexts Applications of portals in healthcare contexts A comprehensive overview that introduces the critical issues regarding the role of IoT technologies for health, Delivering Superior Health and Wellness Management with IoT and Analytics paves the way for scholars, practitioners, students, and other stakeholders to understand how to substantially improve health and wellness management on a global scale.

This book presents models describing HIV transmission rates at population level, discussing the main statistical methods and analytical interventions. It also assesses the practical applicability of the various modelling techniques, offering readers insights into what methods are available and, more importantly, when they should be used to address HIV transmission at global level. The book includes realistic simulation models fitted to clarify the rate of HIV mother-to-child transmission (HIV MTCT), and substantiates the conclusions that can be drawn as well as the appropriate time for making global-level clinical decisions concerning people living with HIV/AIDS (PLHIVs). Intended for students, academics and researchers, the book offers more than just an introduction to the topic - it also features in-depth, yet easy-to-understand, descriptions of a new mathematical/statistical HIV mother-to-child transmission model, making it a useful resource for clinicians, public health workers and policymakers involved in implementing HIV-prevention programmes at national /global level.

This book addresses the difficult task of integrating computational techniques with virtual reality and healthcare. It discusses the use of virtual reality in various areas, such as healthcare, cognitive and behavioural training, understanding mathematical graphs, human-computer interaction, fluid dynamics in healthcare industries, accurate real-time simulation, and healthcare diagnostics. Presenting the computational techniques for virtual reality in healthcare, it is a valuable reference resource for professionals at educational institutes as well as researchers, scientists, engineers and practitioners in industry.

This book offers a snapshot of cutting-edge applications of mobile sensing for digital phenotyping in the field of Psychoinformatics. The respective chapters, written by authoritative researchers, cover various aspects related to the use of these technologies in health, education, and cognitive science research. They share insights both into established applications of mobile sensing (such as predicting personality or mental and behavioral health on the basis of smartphone usage patterns) and emerging trends. Machine learning and deep learning approaches are discussed, and important considerations regarding privacy risks and ethical issues are assessed. In addition to essential background information on various technologies and theoretical methods, the book also presents relevant case studies and good scientific practices, thus addressing researchers and professionals alike. To cite Thomas R. Insel, who wrote the foreword to this book: "Patients will only use digital phenotyping if it solves a problem, perhaps a digital smoke alarm that can prevent a crisis. Providers will only use digital phenotyping if it fits seamlessly into their crowded workflow. If we can earn public trust, there is every reason to be excited about this new field. Suddenly, studying human behavior at scale, over months and years, is feasible."

There are many excellent textbooks that exist in the area of implantable devices for arrhythmia control. Few textbooks, however, provide a case study approach. Collectively, these workbooks provide an approach to problem solving an electrocardiographic interpretation for patients with permanent pacemakers and defibrillators.

Volume 1 addresses patients with permanent pacemakers
Volume 2 examines interactions between pacemakers and implantable defibrillators
Volume 3 provides case studies of transtelephonic pacemakers electrocardiography
Volume 4 provides more pacemaker and implantable defibrillator troubleshooting cases

This volume takes a case-orientated approach, providing a diverse series of problems on a broad range of topics related to implantable defibrillators. The cases are divided into easy, moderate and complex according to their level of difficulty, allowing the reader to gauge their progress. This workbook is suitable for physicians, nurses, technicians, technologists and industry engineers, particularly those new to the field who are keen to learn in a practical way.

This book focuses on interdisciplinary research in the field of biomedical engineering and neuroscience. Biomedical engineering is a vast field, ranging from bioengineering to brain-computer interfaces. The book explores the system-level function and dysfunction of the nervous system from scientific and engineering perspectives. The initial sections introduce readers to the physiology of the brain, and to the biomedical tools needed for diagnostics and effective therapies for various neurodegenerative and regenerative disorders. In turn, the book summarizes the biomedical interventions that are used to understand the neural mechanisms underlying empathy disorders, and reviews recent advances in biomedical engineering for rehabilitation in connection with neurodevelopmental disorders and brain injuries. Lastly, the book discusses innovations in machine learning and artificial intelligence for computer-aided disease diagnosis and treatment, as well as applications of nanotechnology in therapeutic neurology.

This book presents a variety of perspectives on vision-based applications. These contributions are focused on optoelectronic sensors, 3D & 2D machine vision technologies, robot navigation, control schemes, motion controllers, intelligent algorithms and vision systems. The authors focus on applications of unmanned aerial vehicles, autonomous and mobile robots, industrial inspection applications and structural health monitoring. Recent advanced research in measurement and others areas where 3D & 2D machine vision and machine control play an important role, as well as surveys and reviews about vision-based applications. These topics are of interest to readers from diverse areas, including electrical, electronics and computer engineering, technologists, students and non-specialist readers. * Presents current research in image and signal sensors, methods, and 3D & 2D technologies in vision-based theories and applications; * Discusses applications such as daily use devices including robotics, detection, tracking and stereoscopic vision systems, pose estimation, avoidance of objects, control and data exchange for navigation, and aerial imagery processing; * Includes research contributions in scientific, industrial, and civil applications.

This book provides a comprehensive overview of deep learning (DL) in medical and healthcare applications, including the fundamentals and current advances in medical image analysis, state-of-the-art DL methods for medical image analysis and real-world, deep learning-based clinical computer-aided diagnosis systems. Deep learning (DL) is one of the key techniques of artificial intelligence (AI) and today plays an important role in numerous academic and industrial areas. DL involves using a neural network with many layers (deep structure) between input and output, and its main advantage of is that it can automatically learn data-driven, highly representative and hierarchical features and perform feature extraction and classification on one network. DL can be used to model or simulate an intelligent system or process using annotated training data. Recently, DL has become widely used in medical applications, such as anatomic modelling, tumour detection, disease classification, computer-aided diagnosis and surgical planning. This book is intended for computer science and engineering students and researchers, medical professionals and anyone interested using DL techniques.

Data management and analysis is one of the fastest growing and most challenging areas of research and development in both academia and industry. Numerous types of applications and services have been studied and re-examined in this field resulting in this edited volume which includes chapters on effective approaches for dealing with the inherent complexity within data management and analysis. This edited volume contains practical case studies, and will appeal to students, researchers and professionals working in data management and analysis in the business, education, healthcare, and bioinformatics areas.

This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure - and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources - including news media, social media, Google Trends, and Google Street View - can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient.

The rapid and continuous growth in the amount of available medical information and the variety of multimodal content has created demand for a fast and reliable technology capable of processing data and delivering results in a user-friendly manner, whenever and wherever the information is needed. Multimodal acquisition systems, AI-powered applications, and biocybernetic support for medical procedures, physiotherapy and prevention have opened up exciting new avenues in terms of optimizing the healthcare system for the benefit of patients. This book presents a comprehensive study on the latest advances in medical data science and gathers carefully selected articles written by respected experts on information technology. Pursuing an interdisciplinary approach and addressing both theoretical and applied aspects, it chiefly focuses on: Artificial Intelligence Image Analysis Sound and Motion in Physiotherapy and Physioprevention Modeling and Simulation Medical Data Analysis Given its scope, the book offers a valuable reference tool for all scientists who deal with problems of designing and implementing information processing tools employed in systems that assist in patient diagnosis and treatment, as well as students who want to learn more about the latest innovations in quantitative medical data analysis, data mining, and artificial intelligence.

This book highlights the latest advances in the application of artificial intelligence to healthcare and medicine. It gathers selected papers presented at the 2019 Health Intelligence workshop, which was jointly held with the Association for the Advancement of Artificial Intelligence (AAAI) annual conference, and presents an overview of the central issues, challenges, and potential opportunities in the field, along with new research results. By addressing a wide range of practical applications, the book makes the emerging topics of digital health and precision medicine accessible to a broad readership. Further, it offers an essential source of information for scientists, researchers, students, industry professionals, national and international public health agencies, and NGOs interested in the theory and practice of digital and precision medicine and health, with an emphasis on risk factors in connection with disease prevention, diagnosis, and intervention.

This new edition surveys the full range of available structural equation modeling (SEM) methodologies. The book has been updated throughout to reflect the arrival of new software packages, which have made analysis much easier than in the past. Applications in a broad range of disciplines are discussed, particularly in the social sciences where many key concepts are not directly observable. This book presents SEM's development in its proper historical context-essential to understanding the application, strengths and weaknesses of each particular method. This book also surveys the emerging path and network approaches that complement and enhance SEM, and that are growing in importance. SEM's ability to accommodate unobservable theory constructs through latent variables is of significant importance to social scientists. Latent variable theory and application are comprehensively explained and methods are presented for extending their power, including guidelines for data preparation, sample size calculation and the special treatment of Likert scale data. Tables of software, methodologies and fit statistics provide a concise reference for any research program, helping assure that its conclusions are defensible and publishable.

This book presents state-of-the-art research methods, results, and applications in social media and health research. It aims to help readers better understand the different aspects of using social web platforms in health research. Throughout the chapters, the benefits, limitations, and best practices of using a variety of social web platforms in health research are discussed with concrete use cases. This is an ideal book for biomedical researchers, clinicians, and health consumers (including patients) who are interested in learning how social web platforms impact health and healthcare research.