In this volume, scientists and practitioners write about new methods and technologies for improving the operation of health care organizations. Statistical analyses play an important role in these methods with the implications of simulation and modeling applied to the future of health care. Papers are based on work presented at the Second International Conference on Health Care Systems Engineering (HCSE2015) in Lyon, France. The conference was a rare opportunity for scientists and practitioners to share work directly with each other. Each resulting paper received a double blind review. Paper topics include: hospital drug logistics, emergency care, simulation in patient care, and models for home care services.

This book presents the proceedings of the KES International Conferences on Innovation in Medicine and Healthcare (KES-InMed-21), held virtually on June 14-16, 2021. 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.

This book presents advances in biomedical imaging analysis and processing techniques using time dependent medical image datasets for computer aided diagnosis. The analysis of time-series images is one of the most widely appearing problems in science, engineering, and business. In recent years this problem has gained importance due to the increasing availability of more sensitive sensors in science and engineering and due to the wide-spread use of computers in corporations which have increased the amount of time-series data collected by many magnitudes. An important feature of this book is the exploration of different approaches to handle and identify time dependent biomedical images. Biomedical imaging analysis and processing techniques deal with the interaction between all forms of radiation and biological molecules, cells or tissues, to visualize small particles and opaque objects, and to achieve the recognition of biomedical patterns. These are topics of great importance to biomedical science, biology, and medicine. Biomedical imaging analysis techniques can be applied in many different areas to solve existing problems. The various requirements arising from the process of resolving practical problems motivate and expedite the development of biomedical imaging analysis. This is a major reason for the fast growth of the discipline.

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 deals with how to measure innovation in crisis management, drawing on data, case studies, and lessons learnt from different European countries. The aim of this book is to tackle innovation in crisis management through lessons learnt and experiences gained from the implementation of mixed methods through a practitioner-driven approach in a large-scale demonstration project (DRIVER+). It explores innovation from the perspective of the end-users by focusing on the needs and problems they are trying to address through a tool (be it an app, a drone, or a training program) and takes a deep dive into what is needed to understand if and to what extent the tool they have in mind can really bring innovation. This book is a toolkit for readers interested in understanding what needs to be in place to measure innovation: it provides the know-how through examples and best practices. The book will be a valuable source of knowledge for scientists, practitioners, researchers, and postgraduate students studying safety, crisis management, and innovation.

This book embodies principles and applications of advanced soft computing approaches in engineering, healthcare and allied domains directed toward the researchers aspiring to learn and apply intelligent data analytics techniques. The first part covers AI, machine learning and data analytics tools and techniques and their applications to the class of several hospital and health real-life problems. In the later part, the applications of AI, ML and data analytics shall be covered over the wide variety of applications in hospital, health, engineering and/or applied sciences such as the clinical services, medical image analysis, management support, quality analysis, bioinformatics, device analysis and operations. The book presents knowledge of experts in the form of chapters with the objective to introduce the theme of intelligent data analytics and discusses associated theoretical applications. At last, it presents simulation codes for the problems included in the book for better understanding for beginners.

This book describes breath signal processing technologies and their applications in medical sample classification and diagnosis. First, it provides a comprehensive introduction to breath signal acquisition methods, based on different kinds of chemical sensors, together with the optimized selection and fusion acquisition scheme. It then presents preprocessing techniques, such as drift removing and feature extraction methods, and uses case studies to explore the classification methods. Lastly it discusses promising research directions and potential medical applications of computerized breath diagnosis. It is a valuable interdisciplinary resource for researchers, professionals and postgraduate students working in various fields, including breath diagnosis, signal processing, pattern recognition, and biometrics.

The COVID-19 pandemic upended the lives of many and taught us the critical importance of taking care of one's health and wellness. Technological advances, coupled with advances in healthcare, has enabled the widespread growth of a new area called mobile health or mHealth that has completely revolutionized how people envision healthcare today. Just as smartphones and tablet computers are rapidly becoming the dominant consumer computer platforms, mHealth technology is emerging as an integral part of consumer health and wellness management regimes. The aim of this book is to inform readers about the this relatively modern technology, from its history and evolution to the current state-of-the-art research developments and the underlying challenges related to privacy and security issues. The book's intended audience includes individuals interested in learning about mHealth and its contemporary applications, from students to researchers and practitioners working in this field. Both undergraduate and graduate students enrolled in college-level healthcare courses will find this book to be an especially useful companion and will be able to discover and explore novel research directions that will further enrich the field.

This book focuses on recent advances in the Internet of Things (IoT) in biomedical and healthcare technologies, presenting theoretical, methodological, well-established, and validated empirical work in these fields. Artificial intelligence and IoT are set to revolutionize all industries, but perhaps none so much as health care. Both biomedicine and machine learning applications are capable of analyzing data stored in national health databases in order to identify potential health problems, complications and effective protocols, and a range of wearable devices for biomedical and healthcare applications far beyond tracking individuals' steps each day has emerged. These prosthetic technologies have made significant strides in recent decades with the advances in materials and development. As a result, more flexible, more mobile chip-enabled prosthetics or other robotic devices are on the horizon. For example, IoT-enabled wireless ECG sensors that reduce healthcare cost, and lead to better quality of life for cardiac patients. This book focuses on three current trends that are likely to have a significant impact on future healthcare: Advanced Medical Imaging and Signal Processing; Biomedical Sensors; and Biotechnological and Healthcare Advances. It also presents new methods of evaluating medical data, and diagnosing diseases in order to improve general quality of life.

This book describes how the creation of new digital services-through vertical and horizontal integration of data coming from sensors on top of existing legacy systems-that has already had a major impact on industry is now extending to healthcare. The book describes the fourth industrial revolution (i.e. Health 4.0), which is based on virtualization and service aggregation. It shows how sensors, embedded systems, and cyber-physical systems are fundamentally changing the way industrial processes work, their business models, and how we consume, while also affecting the health and care domains. Chapters describe the technology behind the shift of point of care to point of need and away from hospitals and institutions; how care will be delivered virtually outside hospitals; that services will be tailored to individuals rather than being designed as statistical averages; that data analytics will be used to help patients to manage their chronic conditions with help of smart devices; and that pharmaceuticals will be interactive to help prevent adverse reactions. The topics presented will have an impact on a variety of healthcare stakeholders in a continuously global and hyper-connected world. * Presents explanations of emerging topics as they relate to e-health, such as Industry 4.0, Precision Medicine, Mobile Health, 5G, Big Data, and Cyber-physical systems; * Provides overviews of technologies in addition to possible application scenarios and market conditions; * Features comprehensive demographic and statistic coverage of Health 4.0 presented in a graphical manner.

This book provides a comprehensive introduction to computational epidemiology, highlighting its major methodological paradigms throughout the development of the field while emphasizing the needs for a new paradigm shift in order to most effectively address the increasingly complex real-world challenges in disease control and prevention. Specifically, the book presents the basic concepts, related computational models, and tools that are useful for characterizing disease transmission dynamics with respect to a heterogeneous host population. In addition, it shows how to develop and apply computational methods to tackle the challenges involved in population-level intervention, such as prioritized vaccine allocation. A unique feature of this book is that its examination on the issues of vaccination decision-making is not confined only to the question of how to develop strategic policies on prioritized interventions, as it further approaches the issues from the perspective of individuals, offering a well integrated cost-benefit and social-influence account for voluntary vaccination decisions. One of the most important contributions of this book lies in it offers a blueprint on a novel methodological paradigm in epidemiology, namely, systems epidemiology, with detailed systems modeling principles, as well as practical steps and real-world examples, which can readily be applied in addressing future systems epidemiological challenges. The book is intended to serve as a reference book for researchers and practitioners in the fields of computer science and epidemiology. Together with the provided references on the key concepts, methods, and examples being introduced, the book can also readily be adopted as an introductory text for undergraduate and graduate courses in computational epidemiology as well as systems epidemiology, and as training materials for practitioners and field workers.

This book provides a hands-on introduction to Machine Learning (ML) from a multidisciplinary perspective that does not require a background in data science or computer science. It explains ML using simple language and a straightforward approach guided by real-world examples in areas such as health informatics, information technology, and business analytics. The book will help readers understand the various key algorithms, major software tools, and their applications. Moreover, through examples from the healthcare and business analytics fields, it demonstrates how and when ML can help them make better decisions in their disciplines. The book is chiefly intended for undergraduate and graduate students who are taking an introductory course in machine learning. It will also benefit data analysts and anyone interested in learning ML approaches.

This book offers a comprehensive report on the technological aspects of Mobile Health (mHealth) and discusses the main challenges and future directions in the field. It is divided into eight parts: (1) preventive and curative medicine; (2) remote health monitoring; (3) interoperability; (4) framework, architecture, and software/hardware systems; (5) cloud applications; (6) radio technologies and applications; (7) communication networks and systems; and (8) security and privacy mechanisms. The first two parts cover sensor-based and bedside systems for remotely monitoring patients' health condition, which aim at preventing the development of health problems and managing the prognosis of acute and chronic diseases. The related chapters discuss how new sensing and wireless technologies can offer accurate and cost-effective means for monitoring and evaluating behavior of individuals with dementia and psychiatric disorders, such as wandering behavior and sleep impairments. The following two parts focus on architectures and higher level systems, and on the challenges associated with their interoperability and scalability, two important aspects that stand in the way of the widespread deployment of mHealth systems. The remaining parts focus on telecommunication support systems for mHealth, including radio technologies, communication and cloud networks, and secure health-related applications and systems. All in all, the book offers a snapshot of the state-of-art in mHealth systems, and addresses the needs of a multidisciplinary audience, including engineers, computer scientists, healthcare providers, and medical professionals, working in both academia and the industry, as well as stakeholders at government agencies and non-profit organizations.

This book presents recent developments is the field of human aspects in Ambient Intelligence. This field, and the associated workshop series, addresses multidisciplinary aspects of AmI with human-directed disciplines such as psychology, social science, neuroscience and biomedical sciences. The aim of the workshop series is to get researchers together from these human-directed disciplines or working on cross connections of AmI with these disciplines. The focus is on the use of knowledge from these disciplines in AmI applications, in order to support humans in their daily living in medical, psychological and social respects. The book plays important role to get modellers in the psychological, neurological, social or biomedical disciplines interested in AmI as a high-potential application area for their models. From the other side, the book may make researchers in Computer Science and Artificial and Ambient Intelligence more aware of the possibilities to incorporate more substantial knowledge from the psychological, neurological, social and biomedical disciplines in AmI architectures and applications.

This book encapsulates and occupies recent advances and state-of-the-art applications of nature-inspired computing (NIC) techniques in the field of bioinformatics and computational biology, which would aid medical sciences in various clinical applications. This edited volume covers fundamental applications, scope, and future perspectives of NIC techniques in bioinformatics including genomic profiling, gene expression data classification, DNA computation, systems and network biology, solving personalized therapy complications, antimicrobial resistance in bacterial pathogens, and computer-aided drug design, discovery, and therapeutics. It also covers the role of NIC techniques in various diseases and disorders, including cancer detection and diagnosis, breast cancer, lung disorder detection, disease biomarkers, and potential therapeutics identifications.

This book discusses communications technologies used in the field of healthcare, including IoT, soft computing, machine learning, big data, augmented reality, and wearable sensors. The book presents various applications that are helpful for research scholars and scientists who are working toward identifying and pinpointing the potential of this technology. The book also helps researchers and practitioners to understand and analyze the e-healthcare architecture through IoT and the state-of-the-art in IoT countermeasures with real-time challenges. Topics of interest include healthcare systems based on advanced development boards, mobile health parameters recording and monitoring systems, remote health / patient monitoring, hospital operations management, abnormality / disease detection by IoT devices, and efficient drug management. The book is relevant to a range of researchers, academics, and practitioners working on the intersection of IoT and healthcare.

Increase in consumer awareness of nutritional habits has placed automatic food analysis in the spotlight in recent years. However, food-logging is cumbersome and requires sufficient knowledge of the food item consumed. Additionally, keeping track of every meal can become a tedious task. Accurately documenting dietary caloric intake is crucial to manage weight loss, but also presents challenges because most of the current methods for dietary assessment must rely on memory to recall foods eaten. Food understanding from digital media has become a challenge with important applications in many different domains. Substantial research has demonstrated that digital imaging accurately estimates dietary intake in many environments and it has many advantages over other methods. However, how to derive the food information effectively and efficiently remains a challenging and open research problem. The provided recommendations could be based on calorie counting, healthy food and specific nutritional composition. In addition, if we also consider a system able to log the food consumed by every individual along time, it could provide health-related recommendations in the long-term. Computer Vision specialists have developed new methods for automatic food intake monitoring and food logging. Fourth Industrial Revolution [4.0 IR] technologies such as deep learning and computer vision robotics are key for sustainable food understanding. The need for AI based technologies that allow tracking of physical activities and nutrition habits are rapidly increasing and automatic analysis of food images plays an important role. Computer vision and image processing offers truly impressive advances to various applications like food analytics and healthcare analytics and can aid patients in keeping track of their calorie count easily by automating the calorie counting process. It can inform the user about the number of calories, proteins, carbohydrates, and other nutrients provided by each meal. The information is provided in real-time and thus proves to be an efficient method of nutrition tracking and can be shared with the dietician over the internet, reducing healthcare costs. This is possible by a system made up of, IoT sensors, Cloud-Fog based servers and mobile applications. These systems can generate data or images which can be analyzed using machine learning algorithms. Image Based Computing for Food and Health Analytics covers the current status of food image analysis and presents computer vision and image processing based solutions to enhance and improve the accuracy of current measurements of dietary intake. Many solutions are presented to improve the accuracy of assessment by analyzing health images, data and food industry based images captured by mobile devices. Key technique innovations based on Artificial Intelligence and deep learning-based food image recognition algorithms are also discussed. This book examines the usage of 4.0 industrial revolution technologies such as computer vision and artificial intelligence in the field of healthcare and food industry, providing a comprehensive understanding of computer vision and intelligence methodologies which tackles the main challenges of  food and health processing. Additionally, the text focuses on the employing sustainable 4 IR technologies through which consumers can attain the necessary diet and nutrients and can actively monitor their health. In focusing specifically on the food industry and healthcare analytics, it serves as a single source for multidisciplinary information involving AI and vision techniques in the food and health sector. Current advances such as Industry 4.0 and Fog-Cloud based solutions are covered in full, offering readers a fully rounded view of these rapidly advancing health and food analysis systems. 

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 provides a comprehensive overview of various aspects of the development of smart cities from a secure, trusted, and reliable data transmission perspective. It presents theoretical concepts and empirical studies, as well as examples of smart city programs and their capacity to create value for citizens. The contributions offer a panorama of the most important aspects of smart city evolution and implementation within various frameworks, such as healthcare, education, and transportation. Comparing current advanced applications and best practices, the book subsequently explores how smart environments and programs could help improve the quality of life in urban spaces and promote cultural and economic development.

Embedded systems have long become essential in application areas in which human control is impossible or infeasible. The development of modern embedded systems is becoming increasingly difficult and challenging because of their overall system complexity, their tighter and cross-functional integration, the increasing requirements concerning safety and real-time behavior, and the need to reduce development and operation costs. This book provides a comprehensive overview of the Software Platform Embedded Systems (SPES) modeling framework and demonstrates its applicability in embedded system development in various industry domains such as automation, automotive, avionics, energy, and healthcare. In SPES 2020, twenty-one partners from academia and industry have joined forces in order to develop and evaluate in different industrial domains a modeling framework that reflects the current state of the art in embedded systems engineering. The content of this book is structured in four parts. Part I "Starting Point" discusses the status quo of embedded systems development and model-based engineering, and summarizes the key requirements faced when developing embedded systems in different application domains. Part II "The SPES Modeling Framework" describes the SPES modeling framework. Part III "Application and Evaluation of the SPES Modeling Framework" reports on the validation steps taken to ensure that the framework met the requirements discussed in Part I. Finally, Part IV "Impact of the SPES Modeling Framework" summarizes the results achieved and provides an outlook on future work. The book is mainly aimed at professionals and practitioners who deal with the development of embedded systems on a daily basis. Researchers in academia and industry may use it as a compendium for the requirements and state-of-the-art solution concepts for embedded systems development.

The book discusses how augmented intelligence can increase the efficiency and speed of diagnosis in healthcare organizations. The concept of augmented intelligence can reflect the enhanced capabilities of human decision-making in clinical settings when augmented with computation systems and methods. It includes real-life case studies highlighting impact of augmented intelligence in health care. The book offers a guided tour of computational intelligence algorithms, architecture design, and applications of learning in healthcare challenges. It presents a variety of techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. It also presents specific applications of augmented intelligence in health care, and architectural models and frameworks-based augmented solutions.

The book covers the integration of Internet of Things (IoT) and Artificial Intelligence (AI) to tackle applications in smart healthcare. The authors discuss efficient means to collect, monitor, control, optimize, model, and predict healthcare data using AI and IoT. The book presents the many advantages and improvements in the smart healthcare field, in which ubiquitous computing and traditional computational methods alone are often inadequate. AI techniques are presented that play a crucial role in dealing with large amounts of heterogeneous, multi-scale and multi-modal data coming from IoT infrastructures. The book is intended to cover how the fusion of IoT and AI allows the design of models, methodologies, algorithms, evaluation benchmarks, and tools can address challenging problems related to health informatics, healthcare, and wellbeing.

This book gathers extended versions of the best papers presented at the Global Joint Conference on Industrial Engineering and Its Application Areas (GJCIE), held as a hybrid event on October 29-30, 2022, in/from Istanbul Technical University. Continuing the tradition of previous volumes, it highlights recent developments of industrial engineering at the purpose of using and managing digital and intelligent technologies for application to a wide range of field, including manufacturing, healthcare, e-commerce and sustainable development. A special emphasis is given to engineering methods and strategies for managing pandemics and reducing their adverse effects on businesses.

This book introduces the translational informatics applied to most aspects of virus infection, including tracking of virus origin, detection and prevention of infection, drug discovery, and vaccine design as well as smart city-level monitoring and controlling of the virus epidemic by government. It covers the informatics for data mining and modelling at molecular, tissue/organ, individual, and population levels. The informatics for immunological mechanisms and the personalized prediction and treatment of infected patients are also summarized. The perspectives on the application of artificial intelligence to the prevention of virus outbreaks are also given. This book will be helpful to readers who are interested in prevention of virus infection, biomedical informatics, and artificial intelligence in medicine and healthcare.

This book gathers extended contributions to the workshop on Long-Term Digital Care, LTC-2021, organized by the Universita Politecnica delle Marche (UNIVPM), Ancona, Italy, and the Hochschule Konstanz (HTWG), Germany, in November 2021, and funded by the DAAD Joint Mobility Program. It covers innovative, practice-oriented approaches that are expected to foster digital health care, with a special focus on improving internationalization and accessibility. The book, which bridges between technological and social disciplines, reports on selected studies with the main goals of: establishing a comparison of Long-Term Digital Care approaches, with focus on exchange and networking processes; defining practical roadmaps for digital social innovation; establishing concepts and methods for process evaluation and sustainability. It offers a timely snapshot on technologies for patient monitoring and assistant systems, medical data analysis and image processing, digital platforms and advanced diagnostics techniques, and discusses important concepts relating to traceable process evaluation, networking and accessibility. It aims at informing, yet it is also intended to inspire and foster a stronger collaboration across disciplines, countries, as well as academic and professional institutions.