This book presents the latest cutting edge research, theoretical methods, and novel applications in the field of computational intelligence and computational biological approaches that are aiming to combat COVID-19. The book gives the technological key drivers behind using AI to find drugs that target the virus, shedding light on the structure of COVID-19, detecting the outbreak and spread of new diseases, spotting signs of a COVID-19 infection in medical images, monitoring how the virus and lockdown is affecting mental health, and forecasting how COVID-19 cases and deaths will spread across cities and why. Further, the book helps readers understand computational intelligence techniques combating COVID-19 in a simple and systematic way.

This monograph offers a fundamentally new approach to facilitate the study of metabolic networks in cells. It aims to overcome the limitations of either just a single FBA solution, or an overwhelming number of extreme pathways in a realistic network. Instead it focusses on the FBA solution space and describes it in a simplified way by extracting just a bounded subspace: the Solution Space Kernel or SSK. This reduces the relevant number of flux space dimensions by orders of magnitude, and allows its location, size and shape to be characterised. It is a multi-stage process, requiring many new concepts and algorithms for manipulating polytopes in high dimensional spaces.The book introduces and develops these concepts in a pragmatic way that takes into account the difficulties of performing analyses in a flux space with dimensions counting in the hundreds or thousands. It emphasizes the details of implementation in computational code and applications to realistic models are demonstrated. For many cases, the number of constraints and flux variables that fully specify the SSK polytope is only a single or double-digit number. This allows the range of metabolic states accessible to a cell to be further interpreted geometrically in terms of a manageable set of orthogonal diameters and aspect ratios. In addition, explicit representative fluxes, giving the centre and periphery of the solution space kernel, become available for further exploration.

Applied Computing in Medicine and Health is a comprehensive presentation of on-going investigations into current applied computing challenges and advances, with a focus on a particular class of applications, primarily artificial intelligence methods and techniques in medicine and health. Applied computing is the use of practical computer science knowledge to enable use of the latest technology and techniques in a variety of different fields ranging from business to scientific research. One of the most important and relevant areas in applied computing is the use of artificial intelligence (AI) in health and medicine. Artificial intelligence in health and medicine (AIHM) is assuming the challenge of creating and distributing tools that can support medical doctors and specialists in new endeavors. The material included covers a wide variety of interdisciplinary perspectives concerning the theory and practice of applied computing in medicine, human biology, and health care. Particular attention is given to AI-based clinical decision-making, medical knowledge engineering, knowledge-based systems in medical education and research, intelligent medical information systems, intelligent databases, intelligent devices and instruments, medical AI tools, reasoning and metareasoning in medicine, and methodological, philosophical, ethical, and intelligent medical data analysis.

HIMSS' Certified Associate in Healthcare Information and Management Systems (CAHIMS) certification offers a pathway to careers in health information technology (health IT) for associate-level, emerging professionals, or those who would like to transition to health IT from other industries. The CAHIMS Review Guide, 2nd Edition is the ideal resource for those preparing for the CAHIMS certification exam-or looking for a comprehensive "health IT 101" guide. Content in this updated and revised CAHIMS review guide reflects the new CAHIMS exam content outline. Content is divided into three topic categories: organizational and technology environments; systems analysis, design, selection, implementation, support, maintenance, testing, evaluation, privacy, and security; and leadership and management support. Each chapter includes learning objectives for tracking progress in understanding and articulating the content. Practice exam questions at the end of the book reinforce key concepts explored throughout the book. This book is a comprehensive and timely introduction to healthcare information and management systems. It's also an invaluable resource for staying current in all aspects of the industry. In addition to sample exam questions, this book includes an overview of the eligibility requirements, testing procedures, and the CAHIMS examination itself.

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.

Teledentistry is of growing interest to the healthcare world. Over the last few years, momentum is growing in research and service in Teledentistry - mostly carried out by tertiary medical institutes across the world. While Teledentistry is advanced in some sub-specialties, it has high potential to receive more attention from general communities, dentists, dental hygienists, physicians, nurses, researchers and students. For the first time, this book will present essential knowledge from experts in this field. They will discuss the current status of technology and service in various Telledentistry sub specialties and its future implications. Written by experts from around the globe, (i.e., from USA, Europe, Australia and Asia), this book presents technical issues and clinical applications. It includes collective experiences from dental service providers in different parts of the world practicing a wide range of Teledentistry applications. This book lays the foundations for the globalization of Teledentistry procedures, making it possible for dental service to be delivered anywhere in the world.

"True wellness innovation requires the recruitment of multi-disciplinary participants. This book breaks the mold with examples from healthcare experts and other professionals who have leveraged informatics to better the lives of their constituents." - Jason Helgerson, Founder & CEO, Helgerson Solutions Group LLC Developed for those training in academic centers as well as for those already "out in the field," this book looks at how attorneys, behavioral health experts, business development experts, chief information officers, chief medical officers, chief nursing information officers, consumer advocates, cryptographic experts, futurists, geneticists, informaticists, managed care executives, nurses, pharmacists, physicians, public health professionals, software developers, systems security officers, and workforce experts are collaborating on a "team-based," IT-enabled approach to improve healthcare.

Data Science for Effective Healthcare Systems has a prime focus on the importance of data science in the healthcare domain. Various applications of data science in the health care domain have been studied to find possible solutions. In this period of COVID-19 pandemic data science and allied areas plays a vital role to deal with various aspect of health care. Image processing, detection & prevention from COVID-19 virus, drug discovery, early prediction, and prevention of diseases are some thrust areas where data science has proven to be indispensable. Key Features: The book offers comprehensive coverage of the most essential topics, including: Big Data Analytics, Applications & Challenges in Healthcare Descriptive, Predictive and Prescriptive Analytics in Healthcare Artificial Intelligence, Machine Learning, Deep Learning and IoT in Healthcare Data Science in Covid-19, Diabetes, Coronary Heart Diseases, Breast Cancer, Brain Tumor The aim of this book is also to provide the future scope of these technologies in the health care domain. Last but not the least, this book will surely benefit research scholar, persons associated with healthcare, faculty, research organizations, and students to get insights into these emerging technologies in the healthcare domain.

John Glaser has been an astute observer and recognized leader in the health care industry for over thirty years. He has written a regular column for Hospitals & Health Networks in which he comments on a wide range of topics, including improving organizational performance through health information technology (HIT), changes in HIT architecture, challenges in leveraging data, and the evolution of the role of IT leadership. Glaser on Health Care IT: Perspectives from the Decade that Defined Health Care Information Technology is a collection of some of the most widely read articles that have been published in H&HN Daily, H&HN Weekly, and Most Wired Online in the past decade (2005-2015). The columns are dated to show their original publication dates, and the material is organized into four broad themes: HIT Applications and Analytics Challenges Improving Organizational Performance through HIT IT Management Challenges HIT Industry Observations Each section offers readers an intimate look at the myriad issues associated with getting IT "right" and the organizational performance gains that can be achieved in doing so. Moreover, the book examines the power and potential of the technologies available to health care providers today, as well as the transformative nature of those we have yet to fully embrace. From seasoned CIOs and consultants to software developers and nurses, this book provides invaluable insights and guidance to all those seeking to make the delivery of care safer, more effective, and more efficient through the application of health care IT. Foreword by Russ Branzell, President and CEO, College of Healthcare Information Management Executives (CHIME) Co-published with Health Forum, Inc.

Although informatics trainees and practitioners who assume operational computing roles in their organization may have reasonably advanced understanding of theoretical informatics, many are unfamiliar with the practical topics - such as downtime procedures, interface engines, user support, JCAHO compliance, and budgets - which will become the mainstay of their working lives. Practical Guide to Clinical Computing Systems 2nd edition helps prepare these individuals for the electronic age of health care delivery. It is also designed for those who migrate into clinical computing operations roles from within their health care organization. A new group of people interested in this book are those preparing for Clinical Informatics board certification in the US. The work provides particular differentiation from the popular first edition in four areas: 40% more content detailing the many practical aspects of clinical informatics. Addresses the specific needs of the Clinical Informatics board certification course - for which it is presently recommended by the ABPM Focus on new tech paradigms including cloud computing and concurrency - for this rapidly changing field.

In recent years, there have been significant progress in computational intelligence and image processing with machine learning and deep learning as important components of modern artificial intelligence. All these progresses face challenges in dealing with Covid-19 pandemic for detection and treatment.This comprehensive compendium provides not only updated advances of computational intelligence and image processing in the detection and treatment of Covid-19, but also other medical applications such as in cancer detection and cardiovascular diseases, etc. More traditional approaches such as 2D segmentation and 3D reconstruction are included.The useful reference text is an updated version of the edited title, Computer Vision in Medical Imaging (World Scientific, 2014) and its companion volume, Frontiers of Medical Imaging (World Scientific, 2015). The book is written for engineers, scientists and the medical community to meet the increased challenges in medical applications.

This book examines the most novel and state-of-the-art applications of biomaterials, with chapters that exemplify approaches with targeted drug delivery, diabetes, neurodegenerative diseases and cranioplasty implants. Expert contributors analyze biomaterials such as calcium phosphate, sol-gel and quenched glasses, metallic and polymer implants, bioactive glass, and polymer composites while also covering important areas such as the soft tissue replacement, apatites, bone regeneration and cell encapsulation. This book is appropriate for biomedical engineers, materials scientists, and clinicians who are seeking to implement the most advanced approaches and technologies with their patients.

Digital healthcare is heterogeneous along the entire treatment pathway, ranging from monitoring applications and artificial-intelligence-based diagnostics, to support for virtual reality surgery. Since the introduction of the Digital Health Act in Germany in early 2020, there has been a push toward digital innovative solutions, especially in the outpatient sector. This book analyzes current digital health law from an economic perspective, combining theory with real-world applications. It examines both the incentives and market access pathways for digital solutions and the price effects brought about by the new regulatory framework in Germany. Further, it discusses the difficulties in pricing due to the monopolistic BfArM register and negotiations with the association of all German health insurance companies. The book addresses a wide range of topics, including incentives for innovation, specifics of digital health applications, reimbursement and financing options for digital health solutions. Lastly, it presents an outlook for the future and a comparison between Germany and other countries, namely the USA and Japan. Given its scope, this book will appeal to scholars of health economics, healthcare management and public health, as well as practitioners and professionals in the public health sector.

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.

The book, Transformation in Healthcare with Emerging Technologies, presents healthcare industrial revolution based on service aggregation and virtualisation that can transform the healthcare sector with the aid of technologies such as Artificial Intelligence (AI), Internet of Things (IoT), Bigdata and Blockchain. These technologies offer fast communication between doctors and patients, protected transactions, safe data storage and analysis, immutable data records, transparent data flow service, transaction validation process, and secure data exchanges between organizations. Features: * Discusses the Integration of AI, IoT, big data and blockchain in healthcare industry * Highlights the security and privacy aspect of AI, IoT, big data and blockchain in healthcare industry * Talks about challenges and issues of AI, IoT, big data and blockchain in healthcare industry * Includes several case studies It is primarily aimed at graduates and researchers in computer science and IT who are doing collaborative research with the medical industry. Industry professionals will also find it useful.

Presents key aspects in the development and the implementation of machine learning and deep learning approaches towards developing prediction tools, models, and improving medical diagnosis Discusses recent trends innovations, challenges, solutions, and applications of intelligent system-based disease diagnosis Examines deep learning theories, models, and tools to enhance health information systems Explores ML and DL in relation to AI prediction tools discovery of drugs, neuroscience, and diagnosis in multiple imaging modalities

Provides a comprehensive overview on the recent developments on clinical decision support systems, precision health and data science in medicine Examines the advancements, challenges and opportunities of using AI in medical and health applications Includes 10 cases for practical application and reference Reviews melanoma detection by deep learning techniques

Tremendous growth in healthcare treatment techniques and methods has led to the emergence of numerous storage and communication problems and need for security among vendors and patients. This book brings together latest applications and state-of-the-art developments in healthcare sector using Blockchain technology. It explains how blockchain can enhance security, privacy, interoperability, and data accessibility including AI with blockchains, blockchains for medical imaging to supply chain management, and centralized management/clearing houses alongside DLT. Features: Includes theoretical concepts, empirical studies and detailed overview of various aspects related to development of healthcare applications from a reliable, trusted, and secure data transmission perspective. Provide insights on business applications of Blockchain, particularly in the healthcare sector. Explores how Blockchain can solve the transparency issues in the clinical research. Discusses AI with Blockchains, ranging from medical imaging to supply chain management. Reviews benchmark testing of AI with Blockchains and its impacts upon medical uses. This book aims at researchers and graduate students in healthcare information systems, computer and electrical engineering.

Covers computational Intelligence techniques like fuzzy sets, artificial neural networks, deep neural networks, and genetic algorithm for Healthcare systems Provides easy understanding concepts like signal and image filtering techniques Includes discussion over filtering and classification problems Details studies with medical signal (ECG, EEG, EMG) and image (X-rays, FMRI, CT) datasets Describes evolution parameters such as signal-to-noise ratio, mean square error, accuracy, precision, and recall

Presents a variety of techniques designed to enhance and empower multi-disciplinary and multi-institutional machine learning research Offers a compendium of current and emerging machine learning paradigms for healthcare informatics and reflects on the diversity and complexity through the use of case studies Provides a panoramic view of data and machine learning techniques and provides an opportunity for novel insights and discovers Explores the theory and practical applications of machine learning in healthcare Includes a guided tour of machine learning algorithms, architecture design, and applications and in interdisciplinary challenges

This open access book offers a detailed account of a range of mHealth initiatives across South, Southeast and East Asia. It provides readers with deep insights into the challenges such initiatives face on the ground, and a view of the diverse cultural contexts shaping strategies for overcoming these challenges. The book brings together various discussions on the broader mHealth literature, and demonstrates how a research focus on diverse Asian contexts influences the success and/or failure of current mHealth initiatives. It also highlights the important roles social scientists can play in advancing theoretical approaches, as well as planning, implementing and evaluating mHealth initiatives. The book is a valuable resource for project planners, policy developers in NGOs and government institutions, as well as academics, researchers and students in the fields of public health, communications and development studies.

Explores the role of Artificial Intelligence and Smart Computing in health informatics and healthcare with an emphasis on clinical data management and analysis for precise prediction and prompt action Presents cutting edge tracking, monitoring, real time assistance, and security for IoT in healthcare Discusses broadly on wearable sensors and IoT devices and their role in smart living assistance and energy conservation Describes a system mode and architecture for a clear picture of IoT in healthcare Explains the challenges and opportunities with IoT based healthcare industries and includes a study of threats, impacts, and the need of information security

Covers the fundamentals of Machine Learning and Deep Learning in the context of healthcare applications Discusses various data collection approaches from various sources and how to use them in Machine Learning/Deep Learning models Integrates several aspects of AI-based Computational Intelligence like Machine Learning and Deep Learning from diversified perspectives which describe recent research trends and advanced topics in the field Explores the current and future impacts of pandemics and risk mitigation in healthcare with advanced analytics Emphazises feature selection as an important step in any accurate model simulation, ML/DL methods are used to help train the system and extract the positive solution implicitly