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.

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.

Elgar Advanced Introductions are stimulating and thoughtful introductions to major fields in the social sciences, business and law, expertly written by the world's leading scholars. Designed to be accessible yet rigorous, they offer concise and lucid surveys of the substantive and policy issues associated with discrete subject areas. Providing a comprehensive overview of the current and future uses of Artificial Intelligence (AI) in healthcare, this Advanced Introduction discusses the issues surrounding the implementation, governance, impacts and risks of utilising AI in health organizations Key Features: Advises healthcare executives on how to effectively leverage AI to advance their strategies and plans and support digital transformation Discusses AI governance, change management, workforce management and the organization of AI experimentation and implementation Analyzes AI technologies in healthcare and their impacts on patient care, medical devices, pharmaceuticals, population health, and healthcare operations Provides risk mitigation approaches to address potential AI algorithm problems, liability and regulation Essential reading for policymakers, clinical executives and consultants in healthcare, this Advanced Introduction explores how to successfully integrate AI into healthcare organizations and will also prove invaluable to students and scholars interested in technological innovations in healthcare.

Computational Modeling of Infectious Diseases: With Applications in Python provides an illustrated compendium of tools and tactics for analyzing infectious diseases using cutting-edge computational methods. From simple S(E)IR models, and through time series analysis and geospatial models, this book is both a guided tour through the computational analysis of infectious diseases and a quick-reference manual. Chapters are accompanied by extensive practical examples in Python, illustrating applications from start to finish. This book is designed for researchers and practicing infectious disease forecasters, modelers, data scientists, and those who wish to learn more about analysis of infectious disease processes in the real world.

Biostatistics Manual for Health Research: A Practical Guide to Data Analysis is a guide for researchers on how to apply biostatistics on different types of data. The book approaches biostatistics and its application from medical and health researcher's point-of-view and has real and mostly published data for practice and understanding. The interpretation and meaning of the statistical results, reporting guidelines and mistakes are taught with real world examples. This is a valuable resource for biostaticians, students and researchers from medical and biomedical fields who need to learn how to apply statistical approaches to improve their research.

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.

Computational Methods and Deep Learning for Ophthalmology presents readers with the concepts and methods needed to design and use advanced computer-aided diagnosis systems for ophthalmologic abnormalities in the human eye. Chapters cover computational approaches for diagnosis and assessment of a variety of ophthalmologic abnormalities. Computational approaches include topics such as Deep Convolutional Neural Networks, Generative Adversarial Networks, Auto Encoders, Recurrent Neural Networks, and modified/hybrid Artificial Neural Networks. Ophthalmological abnormalities covered include Glaucoma, Diabetic Retinopathy, Macular Degeneration, Retinal Vein Occlusions, eye lesions, cataracts, and optical nerve disorders. This handbook provides biomedical engineers, computer scientists, and multidisciplinary researchers with a significant resource for addressing the increase in the prevalence of diseases such as Diabetic Retinopathy, Glaucoma, and Macular Degeneration.

Unleashing the Potentials of Blockchain Technology for Healthcare Industries discusses blockchain and its adaptation in healthcare industries to provide a secured framework to safeguard healthcare data, both patient and hospital data. The book integrates key pillars of blockchain such as foundations, architecture, smart contracts, adoption, standards, service (BaaS), security, consensus algorithms, drug discovery process, among others, for fortifying the current practices in the healthcare industries. In addition, it offers solutions to the pressing issues currently being faced by the healthcare processes due to the COVD-19 pandemic. This will be a valuable resource for medical informaticians, researchers, healthcare professionals and members of the biomedical field who are interested in learning more about the potentials of blockchain in healthcare.

The Pacific Symposium on Biocomputing (PSB) 2023 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2023 will be held on January 3-7, 2023 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2023 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field.

Translating Epigenetics to the Clinic reviews current methodological tools and experimental approaches used by leading translational researchers seeking to use epigenetics as a clinical model. It organizes epigenetics into disease treatment areas with a major focus on oncology, and with much coverage of pervasive treatment categories such as diabetes, as well as the 'diseases of modernity'-including pharmacological addiction, dementia, and ageing. Pedagogically, the work concentrates on the latest knowledge, laboratory techniques, and experimental approaches used by translational research leaders in this field. The book promotes cross-disciplinary communication between the sub-specialties of medicine. In common with the rest of the books in Translational Medicine, the book remains unified in theme by emphasizing recent innovations, critical barriers to progress, and the new tools being used to overcome them. Also includes specific areas of research that require additional study to advance the field as a whole.

Hyperpolarized and Inert Gas MRI: Theory and Applications in Research and Medicine is the first comprehensive volume published on HP gas MRI. Since the 1990's, when HP gas MRI was invented by Dr. Albert and his colleagues, the HP gas MRI field has grown dramatically. The technique has proven to be a useful tool for diagnosis, disease staging, and therapy evaluation for obstructive lung diseases, including asthma, chronic obstructive pulmonary disease (COPD), and cystic fibrosis. HP gas MRI has also been developed for functional imaging of the brain and is presently being developed for molecular imaging, including molecules associated with lung cancer, breast cancer, and Alzheimer's disease. Taking into account the ongoing growth of this field and the potential for future clinical applications, the book pulls together the most relevant and cutting-edge research available in HP gas MRI into one resource.