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Big data plays an increasingly important role in today's practice of otolaryngology and in all of healthcare. In Big Data in Otolaryngology, Dr. Jennifer Villwock leads a team of expert authors who provide a comprehensive view of many key impacts of big data in otolaryngology—including understanding what big data is and what we can and cannot learn from it; best practices regarding analysis; translating findings to clinical care and associated cautions; ethical issues; and future directions. - Covers the clinical relevance of big data in otolaryngology, lessons and limitations of large administrative datasets, biologic big data, and much more. - Discusses artificial intelligence (AI) in otolaryngology and its clinical application. - Presents a patient perspective on big data in otolaryngology and its use in clinical care, as well as a glimpse into the future of big data. - Compiles the knowledge and expertise of leading experts in the field who have assembled the most up-to-date recommendations for managing big data in otolaryngology. - Consolidates today's available information on this timely topic into a single, convenient resource.
In this issue of Otolaryngologic Clinics, guest editors Drs. Anais Rameau and Matthew G. Crowson bring their considerable expertise to the topic of Artificial Intelligence in Otolaryngology. Top experts in the field cover timely topics in the areas of Best Practices, AI Modalities, Implementation and Governance, and Subspecialty AI. - Contains 17 relevant, practice-oriented topics including clinical data/machine learning; generative AI and otolaryngology-head and neck surgery; ethics; AI in otology and neurotology; AI in facial plastic and reconstructive surgery; AI in pediatric otolaryngology; and more. - Provides in-depth clinical reviews on artificial intelligence in otolaryngology, offering actionable insights for clinical practice. - Presents the latest information on this timely, focused topic under the leadership of experienced editors in the field. Authors synthesize and distill the latest research and practice guidelines to create clinically significant, topic-based reviews.
Big data plays an increasingly important role in today's practice of otolaryngology and in all of healthcare. In Big Data in Otolaryngology, Dr. Jennifer Villwock leads a team of expert authors who provide a comprehensive view of many key impacts of big data in otolaryngology-including understanding what big data is and what we can and cannot learn from it; best practices regarding analysis; translating findings to clinical care and associated cautions; ethical issues; and future directions. Covers the clinical relevance of big data in otolaryngology, lessons and limitations of large administrative datasets, biologic big data, and much more. Discusses artificial intelligence (AI) in otolaryngology and its clinical application. Presents a patient perspective on big data in otolaryngology and its use in clinical care, as well as a glimpse into the future of big data. Compiles the knowledge and expertise of leading experts in the field who have assembled the most up-to-date recommendations for managing big data in otolaryngology. Consolidates today's available information on this timely topic into a single, convenient resource.
In this issue of Otolaryngologic Clinics, guest editors Stephen P. Cragle and Eileen H. Dauer bring their considerable expertise to the topic of Business of Otolaryngology. Top experts in the field cover key topics such as Committing Otolaryngology to pay equity and diversity, Coding for optimal payment, E-health & Telemedicine in Otolaryngology, and more. - Contains 15 relevant, practice-oriented topics including Making a major change – changing your practice setting, retirement, and locums; Talking to patients and their families about adverse events – how transparency and empathy can be transformative for all (Michigan Model or CANDOR); Entrepreneurship and Innovation in Otolaryngology; and more. - Provides in-depth clinical reviews on the Business of Otolaryngology, offering actionable insights for clinical practice. - Presents the latest information on this timely, focused topic under the leadership of experienced editors in the field. Authors synthesize and distill the latest research and practice guidelines to create clinically significant, topic-based reviews.
Big Data Analytics and Intelligence is essential reading for researchers and experts working in the fields of health care, data science, analytics, the internet of things, and information retrieval.
"Big Data Analytics is a field that dissects, efficiently extricates data from, or in any case manages informational indexes that are excessively huge or complex to be managed by customary information preparing application programming. Information with numerous cases (lines) offers more noteworthy factual force, while information with higher multifaceted nature may prompt a higher bogus disclosure rate. Enormous information challenges incorporate catching information, information stockpiling, information investigation, search, sharing, move, representation, and questioning, refreshing, data security and data source. Large information was initially connected with three key ideas: volume, variety and velocity. Consequently, huge information regularly incorporates information with sizes that surpass the limit of conventional programming to measure inside a satisfactory time and worth. Current utilization of the term enormous information will in general allude to the utilization of predictive analytics, user behavior analytics, or certain other progressed information investigation techniques that concentrate an incentive from information, and sometimes to a specific size of informational index. There is little uncertainty that the amounts of information now accessible are undoubtedly enormous, however that is not the most important quality of this new information biological system. Investigation of informational indexes can discover new relationships to spot business patterns or models. Researchers, business persons, clinical specialists, promoting and governments consistently meet challenges with huge informational collections in territories including Internet look, fintech, metropolitan informatics, and business informatics. Researchers experience constraints in e-Science work, including meteorology, genomics, connectomics, complex material science reproductions, science and ecological exploration. The main objective of this book is to write about issues, challenges, opportunities, and solutions in novel research projects about big data in various domains. The topics of interest include, but are not limited to: efficient storage, management and sharing large scale of data; novel approaches for analyzing data using big data technologies; implementation of high performance and/or scalable and/or real-time computation algorithms for analyzing big data; usage of various data sources like historical data, social networking media, machine data and crowd-sourcing data; using machine learning, visual analytics, data mining, spatio-temporal data analysis and statistical inference in different domains (with large scale datasets); Legal and ethical issues and solutions for using, sharing and publishing large datasets; and the results of data analytics, security and privacy issues"--
This state-of-the-art Handbook provides an overview of the role of big data analytics in various areas of business and commerce, including accounting, finance, marketing, human resources, operations management, fashion retailing, information systems, and social media. It provides innovative ways of overcoming the challenges of big data research and proposes new directions for further research using descriptive, diagnostic, predictive, and prescriptive analytics.
Since long before computers were even thought of, data has been collected and organized by diverse cultures across the world. Once access to the Internet became a reality for large swathes of the world's population, the amount of data generated each day became huge, and continues to grow exponentially. It includes all our uploaded documents, video, and photos, all our social media traffic, our online shopping, even the GPS data from our cars. 'Big Data' represents a qualitative change, not simply a quantitative one. The term refers both to the new technologies involved, and to the way it can be used by business and government. Dawn E. Holmes uses a variety of case studies to explain how data is stored, analysed, and exploited by a variety of bodies from big companies to organizations concerned with disease control. Big data is transforming the way businesses operate, and the way medical research can be carried out. At the same time, it raises important ethical issues; Holmes discusses cases such as the Snowden affair, data security, and domestic smart devices which can be hijacked by hackers. ABOUT THE SERIES: The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable.