Public Health Intelligence


Book Description

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




Diagnostic Applications of Health Intelligence and Surveillance Systems


Book Description

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.




The CDC Field Epidemiology Manual


Book Description

A NEW AND ESSENTIAL RESOURCE FOR THE PRACTICE OF EPIDEMIOLOGY AND PUBLIC HEALTH The CDC Field Epidemiology Manual is a definitive guide to investigating acute public health events on the ground and in real time. Assembled and written by experts from the Centers for Disease Control and Prevention as well as other leading public health agencies, it offers current and field-tested guidance for every stage of an outbreak investigation -- from identification to intervention and other core considerations along the way. Modeled after Michael Gregg's seminal book Field Epidemiology, this CDC manual ushers investigators through the core elements of field work, including many of the challenges inherent to outbreaks: working with multiple state and federal agencies or multinational organizations; legal considerations; and effective utilization of an incident-management approach. Additional coverage includes: � Updated guidance for new tools in field investigations, including the latest technologies for data collection and incorporating data from geographic information systems (GIS) � Tips for investigations in unique settings, including healthcare and community-congregate sites � Advice for responding to different types of outbreaks, including acute enteric disease; suspected biologic or toxic agents; and outbreaks of violence, suicide, and other forms of injury For the ever-changing public health landscape, The CDC Field Epidemiology Manual offers a new, authoritative resource for effective outbreak response to acute and emerging threats. *** Oxford University Press will donate a portion of the proceeds from this book to the CDC Foundation, an independent nonprofit and the sole entity created by Congress to mobilize philanthropic and private-sector resources to support the Centers for Disease Control and Prevention's critical health protection work. To learn more about the CDC Foundation, visit www.cdcfoundation.org.




Artificial Intelligence and Machine Learning in Public Healthcare


Book Description

This book discusses and evaluates AI and machine learning (ML) algorithms in dealing with challenges that are primarily related to public health. It also helps find ways in which we can measure possible consequences and societal impacts by taking the following factors into account: open public health issues and common AI solutions (with multiple case studies, such as TB and SARS: COVID-19), AI in sustainable health care, AI in precision medicine and data privacy issues. Public health requires special attention as it drives economy and education system. COVID-19 is an example—a truly infectious disease outbreak. The vision of WHO is to create public health services that can deal with abovementioned crucial challenges by focusing on the following elements: health protection, disease prevention and health promotion. For these issues, in the big data analytics era, AI and ML tools/techniques have potential to improve public health (e.g., existing healthcare solutions and wellness services). In other words, they have proved to be valuable tools not only to analyze/diagnose pathology but also to accelerate decision-making procedure especially when we consider resource-constrained regions.




Handbook of Research on Applied Intelligence for Health and Clinical Informatics


Book Description

Currently, informatics within the field of public health is a developing and growing industry. Clinical informatics are used in direct patient care by supplying medical practitioners with information that can be used to develop a care plan. Intelligent applications in clinical informatics facilitates with the technology-based solutions to analyze data or medical images and help clinicians to retrieve that information. Decision models aid with making complex decisions especially in uncertain situations. The Handbook of Research on Applied Intelligence for Health and Clinical Informatics is a comprehensive reference book that focuses on the study of resources and methods for the management of healthcare infrastructure and information. This book provides insights on how applied intelligence with deep learning, experiential learning, and more will impact healthcare and clinical information processing. The content explores the representation, processing, and communication of clinical information in natural and engineered systems. This book covers a range of topics including applied intelligence, medical imaging, telehealth, and decision support systems, and also looks at technologies and tools used in the detection and diagnosis of medical conditions such as cancers, diabetes, heart disease, lung disease, and prenatal syndromes. It is an essential reference source for diagnosticians, medical professionals, imaging specialists, data specialists, IT consultants, medical technologists, academicians, researchers, industrial experts, scientists, and students.




Artificial Intelligence in Behavioral and Mental Health Care


Book Description

Artificial Intelligence in Behavioral and Mental Health Care summarizes recent advances in artificial intelligence as it applies to mental health clinical practice. Each chapter provides a technical description of the advance, review of application in clinical practice, and empirical data on clinical efficacy. In addition, each chapter includes a discussion of practical issues in clinical settings, ethical considerations, and limitations of use. The book encompasses AI based advances in decision-making, in assessment and treatment, in providing education to clients, robot assisted task completion, and the use of AI for research and data gathering. This book will be of use to mental health practitioners interested in learning about, or incorporating AI advances into their practice and for researchers interested in a comprehensive review of these advances in one source. - Summarizes AI advances for use in mental health practice - Includes advances in AI based decision-making and consultation - Describes AI applications for assessment and treatment - Details AI advances in robots for clinical settings - Provides empirical data on clinical efficacy - Explores practical issues of use in clinical settings




Public Health in the Americas


Book Description

This book describes the principal conceptual, methodological, and empirical developments stemming from PAHO and WHO's institutional efforts in public health, which have entailed the broad and committed participation of the Member States. It provides and overview of the status of Essential Public Health Functions (EPHF) in 41 countries and territories of the Americas, based on self-evaluation exercises performed by health authorities to measure their performance.




Transforming Public Health Surveillance - E-Book


Book Description

Public Health Surveillance (PHS) is of primary importance in this era of emerging health threats like Ebola, MERS-CoV, influenza, natural and man-made disasters, and non-communicable diseases. Transforming Public Health Surveillance is a forward-looking, topical, and up-to-date overview of the issues and solutions facing PHS. It describes the realities of the gaps and impediments to efficient and effective PHS, while presenting a vision for its possibilities and promises in the 21st century. The book gives a roadmap to the goal of public health information being available, when it is needed and where it is needed. Led by Professor Scott McNabb, a leader in the field, an international team of the top-notch public health experts from academia, government, and non-governmental organizations provides the most complete and current update on this core area of public health practice in a decade in 32 chapters. This includes the key roles PHS plays in achieving the global health security agenda and health equity. The authors provide a global perspective for students and professionals in public health. Seven scenarios lay out an aid to understand the context for the lessons of the book, and a comprehensive glossary, questions, bullet points, and learning objectives make this book an excellent tool in the classroom.




Artificial Intelligence for the Internet of Health Things


Book Description

This book discusses research in Artificial Intelligence for the Internet of Health Things. It investigates and explores the possible applications of machine learning, deep learning, soft computing, and evolutionary computing techniques in design, implementation, and optimization of challenging healthcare solutions. This book features a wide range of topics such as AI techniques, IoT, cloud, wearables, and secured data transmission. Written for a broad audience, this book will be useful for clinicians, health professionals, engineers, technology developers, IT consultants, researchers, and students interested in the AI-based healthcare applications. Provides a deeper understanding of key AI algorithms and their use and implementation within the wider healthcare sector Explores different disease diagnosis models using machine learning, deep learning, healthcare data analysis, including machine learning, and data mining and soft computing algorithms Discusses detailed IoT, wearables, and cloud-based disease diagnosis model for intelligent systems and healthcare Reviews different applications and challenges across the design, implementation, and management of intelligent systems and healthcare data networks Introduces a new applications and case studies across all areas of AI in healthcare data K. Shankar (Member, IEEE) is a Postdoctoral Fellow of the Department of Computer Applications, Alagappa University, Karaikudi, India. Eswaran Perumal is an Assistant Professor of the Department of Computer Applications, Alagappa University, Karaikudi, India. Dr. Deepak Gupta is an Assistant Professor of the Department Computer Science & Engineering, Maharaja Agrasen Institute of Technology (GGSIPU), Delhi, India.




Artificial Intelligence in Medicine


Book Description

This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.