Artificial Intelligence for Healthcare Applications and Management introduces application domains of various AI algorithms across healthcare management. Instead of discussing AI first and then exploring its applications in healthcare afterward, the authors attack the problems in context directly, in order to accelerate the path of an interested reader toward building industrial-strength healthcare applications. Readers will be introduced to a wide spectrum of AI applications supporting all stages of patient flow in a healthcare facility. The authors explain how AI supports patients throughout a healthcare facility, including diagnosis and treatment recommendations needed to get patients from the point of admission to the point of discharge while maintaining quality, patient safety, and patient/provider satisfaction. AI methods are expected to decrease the burden on physicians, improve the quality of patient care, and decrease overall treatment costs. Current conditions affected by COVID-19 pose new challenges for healthcare management and learning how to apply AI will be important for a broad spectrum of students and mature professionals working in medical informatics. This book focuses on predictive analytics, health text processing, data aggregation, management of patients, and other fields which have all turned out to be bottlenecks for the efficient management of coronavirus patients.

Contemporary Management of Metastatic Colorectal Cancer: A Precision Medicine Approach summarizes current knowledge and provides evidenced-based practice recommendations on how to treat patients with metastatic colorectal cancer. The book presents topics such as pre-operating imaging, the use of molecular markers in treatment decisions, neoadjuvant therapy, synchronous colorectal liver metastasis, and minimally invasive approaches. In addition, it discusses immunotherapy, targeted therapies and survivorship. This is a valuable resource for practitioners, cancer researchers, oncologists, graduate students and members of biomedical research who need to understand more about novel treatments for colorectal cancer metastasis.

Computational Intelligence in Healthcare Applications discusses a variety of techniques designed to represent, enhance and empower inter-domain research based on computational intelligence in healthcare. The book serves as a reference for the pervasive healthcare domain which takes into consideration new convergent computing and other applications. The book discusses topics such as mathematical modeling in medical imaging, predictive modeling based on artificial intelligence and deep learning, smart healthcare and wearable devices, and evidence-based predictive modeling. In addition, it discusses computer-aided diagnostic for clinical inferences and pervasive and ubiquitous techniques in healthcare. This book is a valuable resource for graduate students and researchers in medical informatics, however, it is also ideal for members of the biomedical field and healthcare industry who are interested in learning more about novel technologies and their applications in the field.

Nowadays, Virtual Reality (VR) is commonly used in various applications including entertainment, education and training, manufacturing, medical and rehabilitation. VR not only provides immersive stereoscopic visualization of virtual environments and the visualization effect and computer graphics are critical to enhancing the engagement of participants and thus increases education and training effectiveness. Nevertheless, constructing realistic 3D models and scenarios for a specific application of VR simulation is not an easy task. There are many different tools for 3D modelling such as ZBrush, Blender, SketchUp, AutoCAD, SolidWorks, 3Ds Max, Maya, Rhino3D, CATIA, and more. Many of the modelling tools are very professional and used for manufacturing and product design application. The advanced features and functions may not be applicable to different levels of users and various specialization. This book explores the application of virtual reality in healthcare settings. This includes 3D modelling techniques, texturing, assigning material, and more. It allows for not only modelling and rendering techniques, but modelling, dressing, and animation in healthcare applications. The potential market of readers, including those from the engineering disciplines such as computer sciences/ computer engineering, product designers, and more. Other potential readers are those studying nursing and medicine, healthcare workers, and anyone interested in the development of VR applications for industry use. In addition, this is suitable for readers from other industries that may need to apply virtual reality in their field.

The cybersecurity of connected medical devices is one of the biggest challenges facing healthcare today. The compromise of a medical device can result in severe consequences for both patient health and patient data. Cybersecurity for Connected Medical Devices covers all aspects of medical device cybersecurity, with a focus on cybersecurity capability development and maintenance, system and software threat modeling, secure design of medical devices, vulnerability management, and integrating cybersecurity design aspects into a medical device manufacturer's Quality Management Systems (QMS). This book is geared towards engineers interested in the medical device cybersecurity space, regulatory, quality, and human resources specialists, and organizational leaders interested in building a medical device cybersecurity program.

Bioinspired and Biomimetic Materials for Drug Delivery delves into the potential of bioinspired materials in drug delivery, detailing each material type and its latest developments. In the last decade, biomimetic and bioinspired materials and technology has garnered increased attention in drug delivery research. Various material types including polymer, small molecular, protein, peptide, cholesterol, polysaccharide, nano-crystal and hybrid materials are widely considered in drug delivery research. However, biomimetic and bioinspired materials and technology have shown promising results for use in therapeutics, due to their high biocompatibility and reduced immunogenicity. Such materials include dopamine, extracellular exosome, bile acids, ionic liquids, and red blood cell. This book covers each of these materials in detail, reviewing their potential and usage in drug delivery. As such, this book will be a great source of information for biomaterials scientists, biomedical engineers and those working in pharmaceutical research.

Deep Learning for Chest Radiographs enumerates different strategies implemented by the authors for designing an efficient convolution neural network-based computer-aided classification (CAC) system for binary classification of chest radiographs into "Normal" and "Pneumonia." Pneumonia is an infectious disease mostly caused by a bacteria or a virus. The prime targets of this infectious disease are children below the age of 5 and adults above the age of 65, mostly due to their poor immunity and lower rates of recovery. Globally, pneumonia has prevalent footprints and kills more children as compared to any other immunity-based disease, causing up to 15% of child deaths per year, especially in developing countries. Out of all the available imaging modalities, such as computed tomography, radiography or X-ray, magnetic resonance imaging, ultrasound, and so on, chest radiographs are most widely used for differential diagnosis between Normal and Pneumonia. In the CAC system designs implemented in this book, a total of 200 chest radiograph images consisting of 100 Normal images and 100 Pneumonia images have been used. These chest radiographs are augmented using geometric transformations, such as rotation, translation, and flipping, to increase the size of the dataset for efficient training of the Convolutional Neural Networks (CNNs). A total of 12 experiments were conducted for the binary classification of chest radiographs into Normal and Pneumonia. It also includes in-depth implementation strategies of exhaustive experimentation carried out using transfer learning-based approaches with decision fusion, deep feature extraction, feature selection, feature dimensionality reduction, and machine learning-based classifiers for implementation of end-to-end CNN-based CAC system designs, lightweight CNN-based CAC system designs, and hybrid CAC system designs for chest radiographs. This book is a valuable resource for academicians, researchers, clinicians, postgraduate and graduate students in medical imaging, CAC, computer-aided diagnosis, computer science and engineering, electrical and electronics engineering, biomedical engineering, bioinformatics, bioengineering, and professionals from the IT industry.

Computational Intelligence and Its Applications in Healthcare presents rapidly growing applications of computational intelligence for healthcare systems, including intelligent synthetic characters, man-machine interface, menu generators, user acceptance analysis, pictures archiving, and communication systems. Computational intelligence is the study of the design of intelligent agents, which are systems that act intelligently: they do what they think are appropriate for their circumstances and goals; they're flexible to changing environments and goals; they learn from experience; and they make appropriate choices given perceptual limitations and finite computation. Computational intelligence paradigms offer many advantages in maintaining and enhancing the field of healthcare.

Virtual Reality (VR) is the use of computer technology to construct an environment that is simulated. VR places the user inside and in the center of the experience, unlike conventional user interfaces. Users are immersed and able to connect with 3D environments instead of seeing a screen in front of them. The computer has to role to provide the experiences of the user in this artificial environment by simulating as many senses as possible, such as sight, hearing, touch and smell. In Augmented Reality (AR) we have an enhanced version of the real physical world that is achieved through the use of digital visual elements, sound, or other sensory stimuli delivered via technology. It can be seen as VR imposed into real life. In both VR and AR the experience is composed of a virtual or extended world, an immersion technology, sensory feedback and interactivity. These elements use a multitude of technologies that must work together and presented to the user seamlessly integrated and synchronized. This book is dedicated to applications, new technologies and emerging trends in the fields of virtual reality and augmented reality in healthcare. It is intended to cover technical areas as well as areas of applied intervention. It is expected to cover hardware and software technologies while encompassing all components of the virtual experience. The main goal of this book is to show how to put Virtual Reality in action by linking academic and informatics researchers with professionals who use and need VR in their day-a-day work, with a special focus on healthcare professionals and related areas. The idea is to disseminate and exchange the knowledge, information and technology provided by the international communities in the area of VR, AR and XR throughout the 21st century. Another important goal is to synthesize all the trends, best practices, methodologies, languages and tools which are used to implement VR. In order to shape the future of VR, new paradigms and technologies should be discussed, not forgetting aspects related to regulation and certification of VR technologies, especially in the healthcare area. These last topics are crucial for the standardization of VR. This book will present important achievements and will show how to use VR technologies in a full range of settings able to provide decision support anywhere and anytime using this new approach.

Computational Retinal Image Analysis: Tools, Applications and Perspectives gives an overview of contemporary retinal image analysis (RIA) in the context of healthcare informatics and artificial intelligence. Specifically, it provides a history of the field, the clinical motivation for RIA, technical foundations (image acquisition modalities, instruments), computational techniques for essential operations, lesion detection (e.g. optic disc in glaucoma, microaneurysms in diabetes) and validation, as well as insights into current investigations drawing from artificial intelligence and big data. This comprehensive reference is ideal for researchers and graduate students in retinal image analysis, computational ophthalmology, artificial intelligence, biomedical engineering, health informatics, and more.

Clinical Engineering: A Handbook for Clinical and Biomedical Engineers, Second Edition, helps professionals and students in clinical engineering successfully deploy medical technologies. The book provides a broad reference to the core elements of the subject, drawing from a range of experienced authors. In addition to engineering skills, clinical engineers must be able to work with both patients and a range of professional staff, including technicians, clinicians and equipment manufacturers. This book will not only help users keep up-to-date on the fast-moving scientific and medical research in the field, but also help them develop laboratory, design, workshop and management skills. The updated edition features the latest fundamentals of medical technology integration, patient safety, risk assessment and assistive technology.

Accelerating Strategic Changes for Digital Transformation in the Healthcare Industry discusses innovative conceptual frameworks, tools, and solutions to successfully tackle the challenges of mitigating the major disruption caused by COVID-19 in the healthcare sector and society. It emphasizes case studies and empirical studies from around the world, providing a comprehensive view of best lessons on digital tools to manage the health crisis. It focuses on the role of advances in digital and collaborative technologies to offer rapid and effective tools for better health solutions for new and emerging health problems. Specially it pays attention to how information technologies help us in the current global health emergency and the coronavirus epidemic response, gaining more understanding of the new coronavirus and helping to contain the outbreak. In addition, it explores how these new tools and digital health solutions can support the economic and social recovery in the post-pandemic world.It is a valuable resource for researchers, students, policy makers and members of the biomedical and medical fields who want to learn more about the transformative role of digital transformation to in the healthcare sector.

Resistance to Anti-CD20 Antibodies and Approaches for Their Reversal presents in-depth content written by international experts in the study of resistance to anti-CD20 antibodies and approaches for their reversal. Anti-CD20 antibodies are used to achieve B cell depletion and are developed to treat B cell proliferative disorders, including non-Hodgkin’s lymphoma and chronic lymphocytic leukemia. In the past two decades, anti-CD20 antibodies have revolutionized the treatment of all B cell malignancies, however, there are patients that fail to respond to initial therapy or relapse sooner. This book explores new and existing avenues surrounding Anti-CD20 antibodies. In recent years, several next-generation anti-CD20 therapies have been developed but predicting and reversing resistance is still a challenging task. These areas are being actively studied as they represent a potential to improve anti-CD20 therapies and are discussed thoroughly in the book. It is a valuable resource for researchers, students and member of the biomedical and medical fields who want to learn more about resistance to anti-CD20 antibodies and their reversal.

Data for Nurses: Understanding and Using Data to Optimize Care Delivery in Hospitals and Health Systems provides information for nurses on how to work with data to effectively evaluate and improve care delivery for patients. Quality, benchmarking, and research data are increasingly used to guide care in hospitals and health systems, and nurses are expected to actively use this information to identify interventions to optimize outcomes and meet reporting and financial targets. However, not all nurses receive formal training on data utilization, making interpretation and application of the different types of data difficult. This book provides information on topics such as benchmarking and reportable indicators, financial metrics, quality improvement, research, and implementation science, with applications to nursing practice. Important information on protective measures to guarantee integrity and security of personal patient data is also included. The book is a valuable resource for nurses and other healthcare professionals who require a basic understanding of key principles of data utilization in order to increase engagement in evidence-based practices, quality improvement, and mandatory reporting of key indices.

ECHNOLOGICAL PROSPECTS AND SOCIAL APPLICATIONS SET Coordinated by Bruno Salgues There are many controversies with respect to health crisis management: the search for information on symptoms, misinformation on emerging treatments, massive use of collaborative tools by healthcare professionals, deployment of applications for tracking infected patients. The Covid-19 crisis is a relevant example about the need for research in digital communications in order to understand current health info communication. After an overview of the challenges of digital healthcare, this book offers a critical look at the organizational and professional limits of ICT uses for patients, their caregivers and healthcare professionals. It analyzes the links between ICT and ethics of care, where health communication is part of a global, humanistic and emancipating care for patients and caregivers. It presents new digitized means of communicating health knowledge that reveal, thanks to the Internet, a competition between biomedical expert knowledge and experiential secular knowledge.

Digital technologies are currently dramatically changing healthcare. Cloud healthcare is an increasingly trending topic in the field, converging skills from computer and health science. This new strategy fosters the management of health data at a large scale and makes it easier for healthcare organizations to improve patient experience and health team productivity while helping the support, security, compliance, and interoperability of health data. Exploring the Convergence of Computer and Medical Science Through Cloud Healthcare is a reference in the ongoing digital transformation of the healthcare sector. It presents a comprehensive state-of-the-art approach to cloud internet of things health technologies and practices. It provides insights over strategies, methodologies, techniques, tools, and services based on emerging cloud digital health solutions to overcome digital health challenges. Covering topics such as auxiliary systems, the internet of medical things, and natural language processing, this premier reference source is an essential resource for medical professionals, hospital administrators, medical students, medical professors, libraries, researchers, and academicians.

Computational Methods in Drug Discovery and Repurposing for Cancer Therapy provides knowledge about ongoing research and computational approaches for drug discovery and repurposing for cancer therapy. The book provides detailed descriptions about target molecules and pathways and their inhibitors for easy understanding and applicability. Users will find discussions on tools and techniques such as integrated bioinformatics approaches, systems biology tools, molecular docking, computational chemistry, artificial intelligence, machine learning, structure-based virtual screening, biomarkers and transcriptome which are discussed in the context of different cancer types, such as colon, glioblastoma, endometrial and retinoblastoma, amongst others. This book will be a valuable resource for researchers, students and member of the biomedical and medical fields who want to learn more about the use of computational modeling to better tailor treatments for cancer patients.