AI and Analytics for Public Health

The Annals of Applied Statistics, 5, 2024–2051. Sezgin, E., Huang, Y., Ramtekkar, U., & Lin, S. (2020). Readiness for voice assistants to support healthcare delivery during a health crisis and pandemic. NPJ Digital Medicine, 3(1), 1–4.

AI and Analytics for Public Health

AI and Analytics for Public Health


Artificial Intelligence and Machine Learning in Public Healthcare

This book discusses and evaluates AI and machine learning (ML) algorithms in dealing with challenges that are primarily related to public health.

Artificial Intelligence and Machine Learning in Public Healthcare

Artificial Intelligence and Machine Learning in Public Healthcare

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.

AI and Analytics for Public Health

This volume offers the state-of-the-art research and developments in service science and related research, education and practice areas.

AI and Analytics for Public Health

AI and Analytics for Public Health

This volume offers the state-of-the-art research and developments in service science and related research, education and practice areas. It showcases emerging technology and applications in fields including healthcare, energy, finance, information technology, transportation, sports, logistics, and public services. Regardless of size and service, a service organization is a service system. Because of the socio-technical nature of a service system, a systems approach must be adopted to design, develop, and deliver services, aimed at meeting end users’ both utilitarian and socio-psychological needs. Effective understanding of service and service systems often requires combining multiple methods to consider how interactions of people, technology, organizations, and information create value under various conditions. Chapters highlight ways to approach such technical challenges in service science and are based on submissions from the 2020 INFORMS International Conference on Service Science.

Artificial Intelligence and Big Data Analytics for Smart Healthcare

a more serious prognosis in terms of expected symptoms and recovery. Patients of older age with underlying health problems such as those of obesity and diabetes may succumb to the disease (Theoharides et al., 2020; Theoharides, 2020; ...

Artificial Intelligence and Big Data Analytics for Smart Healthcare

Artificial Intelligence and Big Data Analytics for Smart Healthcare

Artificial Intelligence and Big Data Analytics for Smart Healthcare serves as a key reference for practitioners and experts involved in healthcare as they strive to enhance the value added of healthcare and develop more sustainable healthcare systems. It brings together insights from emerging sophisticated information and communication technologies such as big data analytics, artificial intelligence, machine learning, data science, medical intelligence, and, by dwelling on their current and prospective applications, highlights managerial and policymaking challenges they may generate. The book is split into five sections: big data infrastructure, framework and design for smart healthcare; signal processing techniques for smart healthcare applications; business analytics (descriptive, diagnostic, predictive and prescriptive) for smart healthcare; emerging tools and techniques for smart healthcare; and challenges (security, privacy, and policy) in big data for smart healthcare. The content is carefully developed to be understandable to different members of healthcare chain to leverage collaborations with researchers and industry. Presents a holistic discussion on the new landscape of data driven medical technologies including Big Data, Analytics, Artificial Intelligence, Machine Learning, and Precision Medicine Discusses such technologies with case study driven approach with reference to real world application and systems, to make easier the understanding to the reader not familiar with them Encompasses an international collaboration perspective, providing understandable knowledge to professionals involved with healthcare to leverage productive partnerships with technology developers

Public Health Informatics and Information Systems

Key Terms in Analytics Definitions for several key terms used in the analytics domain are as follows: • Information ... Artificial intelligence (AI): A subdomain of computer science that focuses on the simulation of a human intelligence ...

Public Health Informatics and Information Systems

Public Health Informatics and Information Systems

This 3rd edition of a classic textbook examines the context and background of public health informatics, explores the technology and science underlying the field, discusses challenges and emerging solutions, reviews many key public health information systems, and includes practical, case-based studies to guide the reader through the topic. The editors have expanded the text into new areas that have become important since publication of the previous two editions due to changing technologies and needs in the field, as well as updating and augmenting much of the core content. The book contains learning objectives, overviews, future directions, and review questions to assist readers to engage with this vast topic. The Editors and their team of well-known contributors have built upon the foundation established by the previous editions to provide the reader with a comprehensive and forward-looking review of public health informatics. The breadth of material in Public Health Informatics and Information Systems, 3rd edition makes it suitable for both undergraduate and graduate coursework in public health informatics, enabling instructors to select chapters that best fit their students’ needs.

Foundations of Artificial Intelligence in Healthcare and Bioscience

To achieve this level of care, doctors are using AI to develop precision treatments with an aim at increasing massive ... analytics. and. AI. in. hospital. care. The field of health care analytics covers a broad range of the healthcare ...

Foundations of Artificial Intelligence in Healthcare and Bioscience

Foundations of Artificial Intelligence in Healthcare and Bioscience

Foundational Handbook of Artificial Intelligence in Healthcare and Bioscience: A User Friendly Guide for IT Professionals, Healthcare Providers, Researchers, and Clinicians uses color-coded illustrations to explain AI from its basics to modern technologies. Other sections cover extensive, current literature research and citations regarding AI’s role in the business and clinical aspects of health care. The book provides readers with a unique opportunity to appreciate AI technology in practical terms, understand its applications, and realize its profound influence on the clinical and business aspects of health care. Artificial Intelligence is a disruptive technology that is having a profound and growing influence on the business of health care as well as medical diagnosis, treatment, research and clinical delivery. The AI relationships in health care are complex, but understandable, especially when discussed and developed from their foundational elements through to their practical applications in health care. Provides an illustrated, foundational guide and comprehensive descriptions of what Artificial Intelligence is and how it functions Integrates a comprehensive discussion of AI applications in the business of health care Presents in-depth clinical and AI-related discussions on diagnostic medicine, therapeutic medicine, and prevalent disease categories with an emphasis on immunology and genetics, the two categories most influenced by AI Includes comprehensive coverage of a variety of AI treatment applications, including medical/pharmaceutical care, nursing care, stem cell therapies, robotics, and 10 common disease categories with AI applications

Sustaining Surveillance The Importance of Information for Public Health

These data are rich resources for the development of analytic and predictive algorithms for the health of ... with novel data sources of particular relevance to public health surveillance: artificial intelligence analytics and bias, ...

Sustaining Surveillance  The Importance of Information for Public Health

Sustaining Surveillance The Importance of Information for Public Health

This book presents a comprehensive theory of the ethics and political philosophy of public health surveillance based on reciprocal obligations among surveillers, those under surveillance, and others potentially affected by surveillance practices. Public health surveillance aims to identify emerging health trends, population health trends, treatment efficacy, and methods of health promotion--all apparently laudatory goals. Nonetheless, as with anti-terrorism surveillance, public health surveillance raises complex questions about privacy, political liberty, and justice both of and in data use. Individuals and groups can be chilled in their personal lives, stigmatized or threatened, and used for the benefit of others when health information is wrongfully collected or used. Transparency and openness about data use, public involvement in decisions, and just distribution of the benefits of surveillance are core elements in the justification of surveillance practices. Understanding health surveillance practices, the concerns it raises, and how to respond to them is critical not only to ethical and trustworthy but also to publicly acceptable and ultimately sustainable surveillance practices. The book is of interest to scholars and practitioners of the ethics and politics of public health, bioethics, privacy and data technology, and health policy. These issues are ever more pressing in pandemic times, where misinformation can travel quickly and suspicions about disease spread, treatment efficacy, and vaccine safety can have devastating public health effects.

Artificial Intelligence in Medicine

Public health surveillance is one such area that has benefited significantly from these recent AI advances. ... Technically, this translates into two distinct challenges: the data sourcing challenge and the analytics challenge.

Artificial Intelligence in Medicine

Artificial Intelligence in Medicine

Artificial Intelligence Medicine: Technical Basis and Clinical Applications presents a comprehensive overview of the field, ranging from its history and technical foundations, to specific clinical applications and finally to prospects. Artificial Intelligence (AI) is expanding across all domains at a breakneck speed. Medicine, with the availability of large multidimensional datasets, lends itself to strong potential advancement with the appropriate harnessing of AI. The integration of AI can occur throughout the continuum of medicine: from basic laboratory discovery to clinical application and healthcare delivery. Integrating AI within medicine has been met with both excitement and scepticism. By understanding how AI works, and developing an appreciation for both limitations and strengths, clinicians can harness its computational power to streamline workflow and improve patient care. It also provides the opportunity to improve upon research methodologies beyond what is currently available using traditional statistical approaches. On the other hand, computers scientists and data analysts can provide solutions, but often lack easy access to clinical insight that may help focus their efforts. This book provides vital background knowledge to help bring these two groups together, and to engage in more streamlined dialogue to yield productive collaborative solutions in the field of medicine. Provides history and overview of artificial intelligence, as narrated by pioneers in the field Discusses broad and deep background and updates on recent advances in both medicine and artificial intelligence that enabled the application of artificial intelligence Addresses the ever-expanding application of this novel technology and discusses some of the unique challenges associated with such an approach

Machine Learning and AI for Healthcare

These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things.

Machine Learning and AI for Healthcare

Machine Learning and AI for Healthcare

Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges. You’ll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization. You’ll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization. Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things. What You'll Learn Gain a deeper understanding of key machine learning algorithms and their use and implementation within wider healthcare Implement machine learning systems, such as speech recognition and enhanced deep learning/AI Select learning methods/algorithms and tuning for use in healthcare Recognize and prepare for the future of artificial intelligence in healthcare through best practices, feedback loops and intelligent agents Who This Book Is For Health care professionals interested in how machine learning can be used to develop health intelligence – with the aim of improving patient health, population health and facilitating significant care-payer cost savings.

Responsible AI and Analytics for an Ethical and Inclusive Digitized Society

Inclusion of technologies like internet of things and artificial intelligence can prove highly beneficial in ... M., Simmons, K.: Tweeting for and against public health policy: response to the Chicago Department of Public Health's ...

Responsible AI and Analytics for an Ethical and Inclusive Digitized Society

Responsible AI and Analytics for an Ethical and Inclusive Digitized Society

This volume constitutes the proceedings of the 20th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2021, held in Galway, Ireland, in September 2021.* The total of 57 full and 8 short papers presented in these volumes were carefully reviewed and selected from 141 submissions. The papers are organized in the following topical sections: AI for Digital Transformation and Public Good; AI & Analytics Decision Making; AI Philosophy, Ethics & Governance; Privacy & Transparency in a Digitized Society; Digital Enabled Sustainable Organizations and Societies; Digital Technologies and Organizational Capabilities; Digitized Supply Chains; Customer Behavior and E-business; Blockchain; Information Systems Development; Social Media & Analytics; and Teaching & Learning. *The conference was held virtually due to the COVID-19 pandemic.

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