This book presents recent work on healthcare management and engineering using artificial intelligence and data mining techniques. Specific topics covered in the contributed chapters include predictive mining, decision support, capacity management, patient flow optimization, image compression, data clustering, and feature selection. The content will be valuable for researchers and postgraduate students in computer science, information technology, industrial engineering, and applied mathematics.

This textbook and guide focuses on methodologies for bias analysis in epidemiology and public health, not only providing updates to the first edition but also further developing methods and adding new advanced methods. As computational power available to analysts has improved and epidemiologic problems have become more advanced, missing data, Bayes, and empirical methods have become more commonly used. This new edition features updated examples throughout and adds coverage addressing: Measurement error pertaining to continuous and polytomous variables Methods surrounding person-time (rate) data Bias analysis using missing data, empirical (likelihood), and Bayes methods A unique feature of this revision is its section on best practices for implementing, presenting, and interpreting bias analyses. Pedagogically, the text guides students and professionals through the planning stages of bias analysis, including the design of validation studies and the collection of validity data from other sources. Three chapters present methods for corrections to address selection bias, uncontrolled confounding, and measurement errors, and subsequent sections extend these methods to probabilistic bias analysis, missing data methods, likelihood-based approaches, Bayesian methods, and best practices.

This significantly revised 5th edition provides nurses with a practical guide to the fundamental concepts of digital health from a nursing perspective. Nursing informatics has never been more important as contemporary healthcare continues to experience tremendous technological advances. The nursing profession is ideally positioned as a key enabler for the design and adoption of emerging eHealth models of care and quality outcomes. The book also features real world examples to illustrate the theory and encourages readers to think critically about their current practices and how they can potentially integrate relevant theories and techniques into their future practice to advance integrated care. Introduction to Nursing Informatics is designed for use as a primer for practicing nurses and students in undergraduate programs of study and includes contributions from leading international experts who have practiced in the field over a number of years. The information is presented and integrated in a purposeful manner to encourage readers to explore the key concepts of nursing practice, digital health, health information management and its relationship to informatics.

This book constitutes revised selected papers from two VLDB workshops: The International Workshop on Polystore Systems for Heterogeneous Data in Multiple Databases with Privacy and Security Assurances, Poly 2021, and the 7th International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2021, which were held virtually on August 2021. For Poly 2021, 7 full and 2 short papers were accepted from 10 submissions; and for DMAH 2021, 4 full papers together with 2 invited papers were accepted from a total of 7 submissions. The papers were organized in topical sections as follows: distributed information systems in enterprises, enterprise access to data constructed from a variety of programming models, data management, data integration, data curation, privacy, and security innovative data management and analytics technologies highlighting end-to-end applications, systems, and methods to address problems in healthcare.

Practical AI for Healthcare Professionals Artificial Intelligence (AI) is a buzzword in the healthcare sphere today. However, notions of what AI actually is and how it works are often not discussed. Furthermore, information on AI implementation is often tailored towards seasoned programmers rather than the healthcare professional/beginner coder. This book gives an introduction to practical AI in the medical sphere, focusing on real-life clinical problems, how to solve them with actual code, and how to evaluate the efficacy of those solutions. You'll start by learning how to diagnose problems as ones that can and cannot be solved with AI. You'll then learn the basics of computer science algorithms, neural networks, and when each should be applied. Then you'll tackle the essential parts of basic Python programming relevant to data processing and making AI programs. The Tensorflow/Keras library along with Numpy and Scikit-Learn are covered as well. Once you've mastered those basic computer science and programming concepts, you can dive into projects with code, implementation details, and explanations. These projects give you the chance to explore using machine learning algorithms for issues such as predicting the probability of hospital admission from emergency room triage and patient demographic data. We will then use deep learning to determine whether patients have pneumonia using chest X-Ray images. The topics covered in this book not only encompass areas of the medical field where AI is already playing a major role, but also are engineered to cover as much as possible of AI that is relevant to medical diagnostics. Along the way, readers can expect to learn data processing, how to conceptualize problems that can be solved by AI, and how to program solutions to those problems. Physicians and other healthcare professionals who can master these skills will be able to lead AI-based research and diagnostic tool development, ultimately benefiting countless patients.

This book presents innovative research works to demonstrate the potential and the advancements of computing approaches to utilize healthcare centric and medical datasets in solving complex healthcare problems. Computing technique is one of the key technologies that are being currently used to perform medical diagnostics in the healthcare domain, thanks to the abundance of medical data being generated and collected. Nowadays, medical data is available in many different forms like MRI images, CT scan images, EHR data, test reports, histopathological data and doctor patient conversation data. This opens up huge opportunities for the application of computing techniques, to derive data-driven models that can be of very high utility, in terms of providing effective treatment to patients. Moreover, machine learning algorithms can uncover hidden patterns and relationships present in medical datasets, which are too complex to uncover, if a data-driven approach is not taken. With the help of computing systems, today, it is possible for researchers to predict an accurate medical diagnosis for new patients, using models built from previous patient data. Apart from automatic diagnostic tasks, computing techniques have also been applied in the process of drug discovery, by which a lot of time and money can be saved. Utilization of genomic data using various computing techniques is another emerging area, which may in fact be the key to fulfilling the dream of personalized medications. Medical prognostics is another area in which machine learning has shown great promise recently, where automatic prognostic models are being built that can predict the progress of the disease, as well as can suggest the potential treatment paths to get ahead of the disease progression.

If your health care organization is typical, you were successful in getting your electronic medical record (EMR) system installed on time and within budget. You declared victory and collected some money from meaningful use. But very quickly, you realized you were not getting the expected return on your investment. So you started the "optimization" process to make refinements, do some stuff over, and get it right this time. The Journey Never Ends: Technology's Role in Helping Perfect Health Care Outcomes is dedicated to helping you derive value from your investment in the software and the people in your organization. It describes some of the major initiatives, post EMR implementation, which most health care organizations now face. Most of these are transformational in nature, and instead of being IT projects, they are business or clinical initiatives with an IT component. If you or your board thought you were done spending large amounts of money on IT after implementing your EMR, you're dead wrong. Welcome to the new reality!

This book reviews the recent research into biological aspects of suicide behavior and outlines each of the varied, recent approaches to prevent suicide. Suicidal behavior, perhaps, is the most complex behavior that combines biological, social, and psychological factors. A new frontier and new opportunities are opening with the technologies of data acquisition and data analysis. Personalized models based on digital phenotype could provide promising strategies for preventing suicide.

This book explores the pivotal role played by technology over the past decade in advancing global public health and health care. At present, the global community faces unprecedented healthcare challenges fueled by an aging population, rising rates of chronic disease, and persistent health disparities. New technologies and advancements have the potential to extend the reach of health professionals while improving quality and efficiency of service delivery and reducing costs within the public and the private health systems. The chapters highlight the barriers faced by the global healthcare workforce in using technology to promote health and human rights of communities: Role of Digital Health, mHealth, and Low-Cost Technologies in Advancing Universal Health Coverage in Emerging Economies Telehealth and Homecare Agencies Technology and the Practice of Health Education in Conflict Zones The Worldwide Digital Divide and Access to Healthcare Technology Technology for Creating Better Professional Teams to Strengthen Healthcare Systems Global Public Health Disaster Management and Technology As a resource on the evolution of technology as a valuable and integral component in the promotion and practice of public health and health care, with a focus on SDG 3 targets, Technology and Global Public Health should engage students, instructors, practitioners, and other professionals interested in public health, universal health care, health technology, digital health, and health equity. Dr. Murthy has been a respected leader and mentor on scientific health-related matters within the UN system for many years. Her book develops a theoretical system connecting concepts that have coined global public health with the rapid development of technology, all with the focus to achieve Sustainable Development Goal number three, within the time frame set by World Leaders. - Henry L. Mac-Donald, Former Permanent Representative of Suriname to the United Nations

This book focuses on signal processing techniques used in computational health informatics. As computational health informatics is the interdisciplinary study of the design, development, adoption and application of information and technology-based innovations, specifically, computational techniques that are relevant in health care, the book covers a comprehensive and representative range of signal processing techniques used in biomedical applications, including: bio-signal origin and dynamics, sensors used for data acquisition, artefact and noise removal techniques, feature extraction techniques in the time, frequency, time-frequency and complexity domain, and image processing techniques in different image modalities. Moreover, it includes an extensive discussion of security and privacy challenges, opportunities and future directions for computational health informatics in the big data age, and addresses the incorporation of recent techniques from the areas of artificial intelligence, deep learning and human-computer interaction. The systematic analysis of the state-of-the-art techniques covered here helps to further our understanding of the physiological processes involved and expandour capabilities in medical diagnosis and prognosis. In closing, the book, the first of its kind, blends state-of-the-art theory and practices of signal processing techniques inthe health informatics domain with real-world case studies building on those theories. As a result, it can be used as a text for health informatics courses to provide medics with cutting-edge signal processing techniques, or to introducehealth professionals who are already serving in this sector to some of the most exciting computational ideas that paved the way for the development of computational health informatics.

Most experts believe that innovation in every aspect of patient care will be nothing less than astonishing as we move into the next century. Technology and the Future of Health Care brings together a remarkable group of health care visionaries who have identified and begun to analyze which trends and technological advances will likely shape and inform the next generation of medicine. From fundamental advances in computing and administration, research, nursing, and patient care delivery to noninvasive surgery, biomolecular therapies, bionics, and beyond, this ground-breaking book offers professional, executive-level insight into topics that until recently existed only in the realm of science fiction.

Resilience has become an important topic on the safety research agenda and in organizational practice. Most empirical work on resilience has been descriptive, identifying characteristics of work and organizing activity which allow organizations to cope with unexpected situations. Fewer studies have developed testable models and theories that can be used to support interventions aiming to increase resilience and improve safety. In addition, the absent integration of different system levels from individuals, teams, organizations, regulatory bodies, and policy level in theory and practice imply that mechanisms through which resilience is linked across complex systems are not yet well understood. Scientific efforts have been made to develop constructs and models that present relationships; however, these cannot be characterized as sufficient for theory building. There is a need for taking a broader look at resilience practices as a foundation for developing a theoretical framework that can help improve safety in complex systems. This book does not advocate for one definition or one field of research when talking about resilience; it does not assume that the use of resilience concepts is necessarily positive for safety. We encourage a broad approach, seeking inspiration across different scientific and practical domains for the purpose of further developing resilience at a theoretical and an operational level of relevance for different high-risk industries. The aim of the book is twofold: 1. To explore different approaches for operationalization of resilience across scientific disciplines and system levels. 2. To create a theoretical foundation for a resilience framework across scientific disciplines and system levels. By presenting chapters from leading international authors representing different research disciplines and practical fields we develop suggestions and inspiration for the research community and practitioners in high-risk industries. This book is Open Access under a CC-BY licence.

This book presents a range of current research topics in biological network modeling, as well as its application in studies on human hosts, pathogens, and diseases. Systems biology is a rapidly expanding field that involves the study of biological systems through the mathematical modeling and analysis of large volumes of biological data. Gathering contributions from renowned experts in the field, some of the topics discussed in depth here include networks in systems biology, the computational modeling of multidrug-resistant bacteria, and systems biology of cancer. Given its scope, the book is intended for researchers, advanced students, and practitioners of systems biology. The chapters are research-oriented, and present some of the latest findings on their respective topics.

The papers in this proceeding discuss current and future trends in wearable communications and personal health management through the use of wireless body area networks (WBAN). The authors posit new technologies that can provide trustworthy communications mechanisms from the user to medical health databases. The authors discuss not only on-body devices, but also technologies providing information in-body. Also discussed are dependable communications combined with accurate localization and behavior analysis, which will benefit WBAN technology and make the healthcare processes more effective. The papers were presented at the 13th EAI International Conference on Body Area Networks (BODYNETS 2018), Oulu, Finland, 02-03 October 2018.

This book provides an overview of the role of AI in medicine and, more generally, of issues at the intersection of mathematics, informatics, and medicine. It is intended for AI experts, offering them a valuable retrospective and a global vision for the future, as well as for non-experts who are curious about this timely and important subject. Its goal is to provide clear, objective, and reasonable information on the issues covered, avoiding any fantasies that the topic "AI" might evoke. In addition, the book seeks to provide a broad kaleidoscopic perspective, rather than deep technical details.

This book provides an insight on the importance that Internet of Things (IoT) and Information and Communication Technology (ICT) solutions can have in taking care of people's health. Key features of this book present the recent and emerging developments in various specializations in curing health problems and finding their solutions by incorporating IoT and ICT. This book presents useful IoT and ICT applications and architectures that cater to their improved healthcare requirements. Topics include in-home healthcare services based on the Internet-of-Things; RFID technology for IoT based personal healthcare; Real-time reporting and monitoring; Interfacing devices to IoT; Smart medical services; Embedded gateway configuration (EGC); Health monitoring infrastructure; and more. Features a number of practical solutions and applications of IoT and ICT on healthcare; Includes application domains such as communication technology and electronic materials and devices; Applies to researchers, academics, students, and practitioners around the world.

This extensively revised 4th edition comprehensively covers information retrieval from a biomedical and health perspective, providing an understanding of the theory, implementation, and evaluation of information retrieval systems in the biomedical and health domain. It features revised chapters covering the theory, practical applications, evaluation and research directions of biomedical and health information retrieval systems. Emphasis is placed on defining where current applications and research systems are heading in a range of areas, including their use by clinicians, consumers, researchers, and others. Information Retrieval: A Biomedical and Health Perspective provides a practically applicable guide to range of techniques for information retrieval and is ideal for use by both the trainee and experienced biomedical informatician seeking an up-to-date resource on the topic.

This book explores various applications of deep learning to the diagnosis of cancer,while also outlining the future face of deep learning-assisted cancer diagnostics. As is commonly known, artificial intelligence has paved the way for countless new solutions in the field of medicine. In this context, deep learning is a recent and remarkable sub-field, which can effectively cope with huge amounts of data and deliver more accurate results. As a vital research area, medical diagnosis is among those in which deep learning-oriented solutions are often employed. Accordingly, the objective of this book is to highlight recent advanced applications of deep learning for diagnosing different types of cancer. The target audience includes scientists, experts, MSc and PhD students, postdocs, and anyone interested in the subjects discussed. The book can be used as a reference work to support courses on artificial intelligence, medical and biomedicaleducation.

This book provides a practically applicable guide to designing evidence-based medical apps and mHealth interventions. It features detailed guidance and case studies where applicable on the best practices and available techniques from both technological (platform technologies, toolkits, sensors) and research perspectives. This approach enables the reader to develop a deep understanding of how to collect the appropriate data and work with users to build a user friendly app for their target audience. Information on how researchers and designers can communicate their intentions with a variety of stakeholders including medical practitioners, developers and researchers to ensure the best possible decisions are made during the development process to produce an app of optimal quality that also considers usability. Developing Medical Apps and mHealth Interventions comprehensively covers the development of medical and health apps for researchers, informaticians and physicians, and is a valuable resource for the experienced professional and trainee seeking a text on how to develop user friendly medical apps.

This book explores various applications of deep learning-oriented diagnosis leading to decision support, while also outlining the future face of medical decision support systems. Artificial intelligence has now become a ubiquitous aspect of modern life, and especially machine learning enjoysgreat popularity, since it offers techniques that are capable of learning from samples to solve newly encountered cases. Today, a recent form of machine learning, deep learning, is being widely used with large, complex quantities of data, because today's problems require detailed analyses of more data. This is critical, especially in fields such as medicine. Accordingly, the objective of this book is to provide the essentials of and highlight recent applications of deep learning architectures for medical decision support systems. The target audience includes scientists, experts, MSc and PhD students, postdocs, and any readers interested in the subjectsdiscussed. The book canbe used as a reference work to support courses on artificial intelligence, machine/deep learning, medical and biomedicaleducation.

Offering an authoritative account of the relationship between literature and medicine between approximately 1800 and 1900, this volume brings together leading scholars in the field to provide a valuable overview of how two dynamic fields influenced and shaped each during a period of revolutionary change. During the nineteenth century, medicine was being redefined as a subject in which experimental methodologies could transform the healing art, and was simultaneously branching off into new specialisms and subdivisions. Questions addressed in this volume include the influence of physics on poetry, the role of medical professionalism in fiction, the cultural and literary representation of sanitation, and the interdisciplinary nature of controversy and negligence. Along with its sister publication, Literature and Medicine in the Eighteenth Century, this volume offers a major critical overview of the study of literature and medicine.

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.