In recent years, scientific research and translation medicine have placed increased emphasis on computational methodology and data curation across many disciplines, both to advance underlying science and to instantiate precision-medicine protocols in the lab and in clinical practice. The nexus of concerns related to oncology, cardiology, and virology (SARS-CoV-2) presents a fortuitous context within which to examine the theory and practice of biomedical data curation. Innovative Data Integration and Conceptual Space Modeling for COVID, Cancer, and Cardiac Care argues that a well-rounded approach to data modeling should optimally embrace multiple perspectives inasmuch as data-modeling is neither a purely formal nor a purely conceptual discipline, but rather a hybrid of both. On the one hand, data models are designed for use by computer software components, and are, consequently, constrained by the mechanistic demands of software environments; data modeling strategies must accept the formal rigors imposed by unambiguous data-sharing and query-evaluation logic. In particular, data models are not well-suited for software-level deployment if such models do not translate seamlessly to clear strategies for querying data and ensuring data integrity as information is moved across multiple points. On the other hand, data modeling is, likewise, constrained by human conceptual tendencies, because the information which is managed by databases and data networks is ultimately intended to be visualized/utilized by humans as the end-user. Thus, at the intersection of both formal and humanistic methodology, data modeling takes on elements of both logico-mathematical frameworks (e.g., type systems and graph theory) and conceptual/philosophical paradigms (e.g., linguistics and cognitive science). The authors embrace this two-sided aspect of data models by seeking non-reductionistic points of convergence between formal and humanistic/conceptual viewpoints, and by leveraging biomedical contexts (viz., COVID, Cancer, and Cardiac Care) so as to provide motivating examples and case-studies in this volume. - Provides an analysis of how conceptual spaces and related cognitive linguistic approaches can inspire programming and query-processing models - Outlines the vital role that data modeling/curation has played in significant medical breakthroughs - Presents readers with an overview of how information-management approaches intersect with precision medicine, providing case studies of data-modeling in concrete scientific practice - Explores applications of image analysis and computer vision in the context of precision medicine - Examines the role of technology in scientific publishing, replication studies, and dataset curation

This book presents some of the most advanced leading-edge technology for the fourth Industrial Revolution -- known as “Industry 4.0.” The book provides a comprehensive understanding of the interconnections of AI, IoT, big data and cloud computing as integral to the technologies that revolutionize the way companies produce and distribute products and the way local governments deliver their services. The book emphasizes that at every phase of the supply chain, manufactures are found to be interweaving AI, robotics, IoT, big data/machine learning, and cloud computing into their production facilities and throughout their distribution networks. Equally important, the authors show how their research can be applied to computer vision, cyber security, database and compiler theory, natural language processing, healthcare, education and agriculture. Presents the fundamentals of AI, IoT, and cloud computing and how they can be incorporated in Industry 4.0 applications Motivates readers to address challenges in the areas of speech communication and signal processing Provides numerous examples, case studies, technical descriptions, and approaches of AI/ML

Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data

Precision Medicine and Artificial Intelligence: The Perfect Fit for Autoimmunity covers background on artificial intelligence (AI), its link to precision medicine (PM), and examples of AI in healthcare, especially autoimmunity. The book highlights future perspectives and potential directions as AI has gained significant attention during the past decade. Autoimmune diseases are complex and heterogeneous conditions, but exciting new developments and implementation tactics surrounding automated systems have enabled the generation of large datasets, making autoimmunity an ideal target for AI and precision medicine. More and more diagnostic products utilize AI, which is also starting to be supported by regulatory agencies such as the Food and Drug Administration (FDA). Knowledge generation by leveraging large datasets including demographic, environmental, clinical and biomarker data has the potential to not only impact the diagnosis of patients, but also disease prediction, prognosis and treatment options. - Allows the readers to gain an overview on precision medicine for autoimmune diseases leveraging AI solutions - Provides background, milestone and examples of precision medicine - Outlines the paradigm shift towards precision medicine driven by value-based systems - Discusses future applications of precision medicine research using AI - Other aspects covered in the book include regulatory insights, data analytics and visualization, types of biomarkers as well as the role of the patient in precision medicine

In the United States, some populations suffer from far greater disparities in health than others. Those disparities are caused not only by fundamental differences in health status across segments of the population, but also because of inequities in factors that impact health status, so-called determinants of health. Only part of an individual's health status depends on his or her behavior and choice; community-wide problems like poverty, unemployment, poor education, inadequate housing, poor public transportation, interpersonal violence, and decaying neighborhoods also contribute to health inequities, as well as the historic and ongoing interplay of structures, policies, and norms that shape lives. When these factors are not optimal in a community, it does not mean they are intractable: such inequities can be mitigated by social policies that can shape health in powerful ways. Communities in Action: Pathways to Health Equity seeks to delineate the causes of and the solutions to health inequities in the United States. This report focuses on what communities can do to promote health equity, what actions are needed by the many and varied stakeholders that are part of communities or support them, as well as the root causes and structural barriers that need to be overcome.

The World Health Organization stated that approximately two-thirds of the world’s population lacks adequate access to medical imaging. The scarcity of imaging services in developing regions contributes to a widening disparity of health care and limits global public health programs that require imaging. Radiology is an important component of many global health programs, including those that address tuberculosis, AIDS-related disease, trauma, occupational and environmental exposures, breast cancer screening, and maternal-infant health care. There is a growing need for medical imaging in global health efforts and humanitarian outreach, particularly as an increasing number of academic, government, and non-governmental organizations expand delivery of health care to disadvantaged people worldwide. To systematically deploy clinical imaging services to low-resource settings requires contributions from a variety of disciplines such as clinical radiology, epidemiology, public health, finance, radiation physics, information technology, engineering, and others. This book will review critical concepts for those interested in managing, establishing, or participating in a medical imaging program for resource-limited environments and diverse cross-cultural contexts undergoing imaging technology adaptation.

High-quality primary care is the foundation of the health care system. It provides continuous, person-centered, relationship-based care that considers the needs and preferences of individuals, families, and communities. Without access to high-quality primary care, minor health problems can spiral into chronic disease, chronic disease management becomes difficult and uncoordinated, visits to emergency departments increase, preventive care lags, and health care spending soars to unsustainable levels. Unequal access to primary care remains a concern, and the COVID-19 pandemic amplified pervasive economic, mental health, and social health disparities that ubiquitous, high-quality primary care might have reduced. Primary care is the only health care component where an increased supply is associated with better population health and more equitable outcomes. For this reason, primary care is a common good, which makes the strength and quality of the country's primary care services a public concern. Implementing High-Quality Primary Care: Rebuilding the Foundation of Health Care puts forth an evidence-based plan with actionable objectives and recommendations for implementing high-quality primary care in the United States. The implementation plan of this report balances national needs for scalable solutions while allowing for adaptations to meet local needs.