Health surveillance and intelligence play an important role in modern health systems as more data must be collected and analyzed. It is crucial that this data is interpreted and analyzed effectively and efficiently in order to assist with diagnoses and predictions. Diagnostic Applications of Health Intelligence and Surveillance Systems is an essential reference book that examines recent studies that are driving artificial intelligence in the health sector and helping practitioners to predict and diagnose diseases. Chapters within the book focus on health intelligence and how health surveillance data can be made useful and meaningful. Covering topics that include computational intelligence, data analytics, mobile health, and neural networks, this book is crucial for healthcare practitioners, IT specialists, academicians, researchers, and students.

This fully updated edition of Infectious Disease Surveillance is for frontline public health practitioners, epidemiologists, and clinical microbiologists who are engaged in communicable disease control. It is also a foundational text for trainees in public health, applied epidemiology, postgraduate medicine and nursing programs. The second edition portrays both the conceptual framework and practical aspects of infectious disease surveillance. It is a comprehensive resource designed to improve the tracking of infectious diseases and to serve as a starting point in the development of new surveillance systems. Infectious Disease Surveillance includes over 45 chapters from over 100 contributors, and topics organized into six sections based on major themes. Section One highlights the critical role surveillance plays in public health and it provides an overview of the current International Health Regulations (2005) in addition to successes and challenges in infectious disease eradication. Section Two describes surveillance systems based on logical program areas such as foodborne illnesses, vector-borne diseases, sexually transmitted diseases, viral hepatitis healthcare and transplantation associated infections. Attention is devoted to programs for monitoring unexplained deaths, agents of bioterrorism, mass gatherings, and disease associated with international travel. Sections Three and Four explore the uses of the Internet and wireless technologies to advance infectious disease surveillance in various settings with emphasis on best practices based on deployed systems. They also address molecular laboratory methods, and statistical and geospatial analysis, and evaluation of systems for early epidemic detection. Sections Five and Six discuss legal and ethical considerations, communication strategies and applied epidemiology-training programs. The rest of the chapters offer public-private partnerships, as well lessons from the 2009-2010 H1N1 influenza pandemic and future directions for infectious disease surveillance.

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

Artificial intelligence (AI) has emerged as a transformative force across various domains, revolutionizing the way we perceive and address challenges in healthcare. The convergence of AI and healthcare holds immense promise, offering unprecedented opportunities to enhance medical diagnosis, treatment, and patient care. In today’s world, the intersection of AI and healthcare stands as one of the most promising frontiers for innovation and progress. Artificial Intelligence Transformations for Healthcare Applications: Medical Diagnosis, Treatment, and Patient Care embodies this convergence, offering a comprehensive exploration of how AI is revolutionizing various aspects of healthcare delivery. At its core, this book addresses the urgent need for more effective and efficient healthcare solutions in an increasingly complex and data-rich environment. Covering topics such as chronic disease, image classification, and precision medicine, this book is an essential resource for healthcare professionals, medical researchers, AI and machine learning specialists, healthcare administrators and executives, medical educators and students, biomedical engineers, healthcare IT professionals, policy makers and regulators, academicians, and more.

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. - Provides coverage of fuzzy logic, neural networks, evolutionary computation, learning theory, probabilistic methods, telemedicine, and robotics applications - Includes coverage of artificial intelligence and biological applications, soft computing, image and signal processing, and genetic algorithms - Presents the latest developments in computational methods in healthcare - Bridges the gap between obsolete literature and current literature

The first textbook on public health intelligence presents in depth the key concepts, methods, and objectives of this increasingly important competency. It systematically reviews types of evidence and data that comprise intelligence, effective techniques for assessment, analysis, and interpretation, and the role of this knowledge in quality health service delivery. The book’s learner-centered approach gives readers interactive context for mastering the processes of gathering and working with intelligence as well as its uses in informing public health decision-making. And its pragmatic framework will help establish standards for training, practice, and policy, leading to continued improvements in population health. This path-breaking resource: Offers a comprehensive, up-to-date introduction to public health intelligence, a core area of public health competency. Is suitable for both graduates’ and healthcare professionals’ training and development for national and international contexts. Helps readers apply theory to real-life scenarios, from multi-professional perspectives. Features activities, case studies, and discussion tasks for easy reader engagement. Anticipates and examines emerging developments in the field. Public Health Intelligence - Issues of Measure and Method is bedrock reading for postgraduate and advanced undergraduate students in public health, global health, health policy, health service management, nursing, medicine, statistics, epidemiology, quantitative methods, health intelligence, health inequality, and other allied healthcare fields. It is also a salient text for public health practitioners and health policymakers. "This book is a 'must-read' for students contemplating a career in Public Health or for anyone who is already in practice. The breadth of chapters from respected authors provide a detailed overview and critique of issues related to public health intelligence. A key strength of the book is that it is written with both students and practitioners in mind." Gurch Randhawa, PhD, FFPH, Professor of Diversity in Public Health & Director, Institute for Health Research, University of Bedfordshire, UK

Getting the right diagnosis is a key aspect of health care - it provides an explanation of a patient's health problem and informs subsequent health care decisions. The diagnostic process is a complex, collaborative activity that involves clinical reasoning and information gathering to determine a patient's health problem. According to Improving Diagnosis in Health Care, diagnostic errors-inaccurate or delayed diagnoses-persist throughout all settings of care and continue to harm an unacceptable number of patients. It is likely that most people will experience at least one diagnostic error in their lifetime, sometimes with devastating consequences. Diagnostic errors may cause harm to patients by preventing or delaying appropriate treatment, providing unnecessary or harmful treatment, or resulting in psychological or financial repercussions. The committee concluded that improving the diagnostic process is not only possible, but also represents a moral, professional, and public health imperative. Improving Diagnosis in Health Care, a continuation of the landmark Institute of Medicine reports To Err Is Human (2000) and Crossing the Quality Chasm (2001), finds that diagnosis-and, in particular, the occurrence of diagnostic errorsâ€"has been largely unappreciated in efforts to improve the quality and safety of health care. Without a dedicated focus on improving diagnosis, diagnostic errors will likely worsen as the delivery of health care and the diagnostic process continue to increase in complexity. Just as the diagnostic process is a collaborative activity, improving diagnosis will require collaboration and a widespread commitment to change among health care professionals, health care organizations, patients and their families, researchers, and policy makers. The recommendations of Improving Diagnosis in Health Care contribute to the growing momentum for change in this crucial area of health care quality and safety.

The multidisciplinary applications of natural science in drug discovery are essential for identifying and developing new drugs and represent a promising area of research. Drug discovery is a complex and challenging process that involves multiple disciplines, including biology, chemistry, pharmacology, and medicine. One of the most interesting areas of drug discovery is using natural science, which involves the study of natural products and their potential therapeutic benefits, in which the research literature has previously been lacking appropriate attention. Multidisciplinary Applications of Natural Science for Drug Discovery and Integrative Medicine is a research book that bridges the gap between traditional and natural science approaches to identify new drug molecules in treating various diseases. The book focuses on allopathic and complementary medicine. It takes a cross-disciplinary examination of biology, chemistry, pharmacology, mathematics, physics, and medicine. This book is suitable for researchers, post-doctoral fellows, graduate students, and post-graduate students interested in exploring natural science's multidisciplinary applications in drug discovery and integrative medicine.

In the intricate tapestry of nature, the potential of kitchen spice seeds remains largely untapped, despite their rich history as sources of natural compounds with diverse benefits. The scientific community faces a challenge in fully comprehending and harnessing the phytochemical, ethnobotanical, and pharmacological dimensions of these seeds, which hold promise in medicine but play pivotal roles in cultural, historical, and culinary spheres. Therapeutic and Pharmacological Applications of Ethnobotany is an all-encompassing compendium meticulously crafted to bridge these knowledge gaps. By presenting a synthesis of phytochemical analysis, ethnobotanical insights, pharmacological studies, botanical descriptions, industrial and culinary applications, biogenic synthesis, and innovative uses, this book provides a holistic exploration of kitchen spice seeds. Tailored for researchers, scientists, botanists, pharmacologists, ethnobotanists, and culinary experts, it serves as a beacon guiding readers through the scientific intricacies and cultural significances of these seeds, unlocking a wealth of knowledge that transcends disciplinary boundaries.

Health professionals grapple with a critical challenge: the traditional Continuous Medical Education (CME) model falls short of fostering the unique skills and self-directed learning required for a dynamic career. As medical practitioners navigate a world of new epidemiological models, technologies, and strategies, the need for a transformative solution becomes evident. The Lifelong Learning Journey of Health Professionals: Continuing Education and Professional Development is a book that not only identifies the limitations of existing education models but also provides a comprehensive solution for ushering in a new era of lifelong learning. This compelling book advocates for a paradigm shift towards Continuous Professional Development (CPD), a contemporary concept that embraces non-traditional learning formats. It dismantles the inadequacies of credit-based training by emphasizing the importance of self-direction and self-assessment for adult learners. From core principles for designing a robust CPD system to exploring successful models, alternative credentials, and the role of learning communities, the book offers a holistic approach to reshaping medical education.