Big Data Analytics in Healthcare, Anand J. Kulkarni; Patrick Siarry; Pramod Kumar Si
Автор: Fusheng Wang; Lixia Yao; Gang Luo Название: Data Management and Analytics for Medicine and Healthcare ISBN: 3319577409 ISBN-13(EAN): 9783319577401 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the thoroughly refereed post-conference proceedings of the Second International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2016, in New Delhi, India, in September 2016, held in conjunction with the 42nd International Conference on Very Large Data Bases, VLDB 2016.
Автор: Alex Lui, Anna Farzinder, Mingboo Gong Название: Transforming Healthcare with Big Data and AI ISBN: 1641138971 ISBN-13(EAN): 9781641138970 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 7069.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Healthcare and technology are at a convergence point where significant changes are poised to take place. The vast and complex requirements of medical record keeping, coupled with stringent patient privacy laws, create an incredibly unwieldy maze of health data needs. While the past decade has seen giant leaps in AI, machine learning, wearable technologies, and data mining capacities that have enabled quantities of data to be accumulated, processed, and shared around the globe. Transforming Healthcare with Big Data and AI examines the crossroads of these two fields and looks to the future of leveraging advanced technologies and developing data ecosystems to the healthcare field.
This book is the product of the Transforming Healthcare with Data conference, held at the University of Southern California. Many speakers and digital healthcare industry leaders contributed multidisciplinary expertise to chapters in this work. Authors’ backgrounds range from data scientists, healthcare experts, university professors, and digital healthcare entrepreneurs. If you have an understanding of data technologies and are interested in the future of Big Data and A.I. in healthcare, this book will provide a wealth of insights into the new landscape of healthcare.
Автор: Alex Lui, Anna Farzinder, Mingboo Gong Название: Transforming Healthcare with Big Data and AI ISBN: 164113898X ISBN-13(EAN): 9781641138987 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 13167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The past decade has seen giant leaps in AI, machine learning, wearable technologies, and data mining capacities. This book examines the crossroads of these two fields and looks to the future of leveraging advanced technologies and developing data ecosystems to the healthcare field.
This book describes how the creation of new digital services--through vertical and horizontal integration of data coming from sensors on top of existing legacy systems--that has already had a major impact on industry is now extending to healthcare. The book describes the fourth industrial revolution (i.e. Health 4.0), which is based on virtualization and service aggregation. It shows how sensors, embedded systems, and cyber-physical systems are fundamentally changing the way industrial processes work, their business models, and how we consume, while also affecting the health and care domains. Chapters describe the technology behind the shift of point of care to point of need and away from hospitals and institutions; how care will be delivered virtually outside hospitals; that services will be tailored to individuals rather than being designed as statistical averages; that data analytics will be used to help patients to manage their chronic conditions with help of smart devices; and that pharmaceuticals will be interactive to help prevent adverse reactions. The topics presented will have an impact on a variety of healthcare stakeholders in a continuously global and hyper-connected world.
- Presents explanations of emerging topics as they relate to e-health, such as Industry 4.0, Precision Medicine, Mobile Health, 5G, Big Data, and Cyber-physical systems;
- Provides overviews of technologies in addition to possible application scenarios and market conditions;
- Features comprehensive demographic and statistic coverage of Health 4.0 presented in a graphical manner.
Описание: Internet of Things Driven Connected Healthcare.-Internet of Things in HealthcareEnergy Efficient Network Design for IoT Healthcare Applications.-Exploring Formal Strategy Framework for the Security in IoT in e-health using Computational Intelligence.-Vitality of Robotics in Healthcare Industry: An Internet of Things (IoT) Perspective.-Internet of Things Meets Mobile Health Systems in Smart Spaces: An Overview.-Information and Communication Emerging Technology: Making Sense of Healthcare Innovation.-Health Informatics as a Service (HIaaS) for Developing Countries.- Analysis of Power Aware Protocols and Standards for Critical E-Health Applications.-Social Networking and Analytics.-A decision support system in brain tumor detection and localization in nominated areas in MR images.-Detecting Unusual Human Activities Using GPU-enabled Neural Network and Kinect Sensors.
This book highlights state-of-the-art research on big data and the Internet of Things (IoT), along with related areas to ensure efficient and Internet-compatible IoT systems. It not only discusses big data security and privacy challenges, but also energy-efficient approaches to improving virtual machine placement in cloud computing environments.
Big data and the Internet of Things (IoT) are ultimately two sides of the same coin, yet extracting, analyzing and managing IoT data poses a serious challenge. Accordingly, proper analytics infrastructures/platforms should be used to analyze IoT data. Information technology (IT) allows people to upload, retrieve, store and collect information, which ultimately forms big data. The use of big data analytics has grown tremendously in just the past few years. At the same time, the IoT has entered the public consciousness, sparking people's imaginations as to what a fully connected world can offer.
Further, the book discusses the analysis of real-time big data to derive actionable intelligence in enterprise applications in several domains, such as in industry and agriculture. It explores possible automated solutions in daily life, including structures for smart cities and automated home systems based on IoT technology, as well as health care systems that manage large amounts of data (big data) to improve clinical decisions.
The book addresses the security and privacy of the IoT and big data technologies, while also revealing the impact of IoT technologies on several scenarios in smart cities design. Intended as a comprehensive introduction, it offers in-depth analysis and provides scientists, engineers and professionals the latest techniques, frameworks and strategies used in IoT and big data technologies.
Автор: Vijay Gadepally; Timothy Mattson; Michael Stonebra Название: Heterogeneous Data Management, Polystores, and Analytics for Healthcare ISBN: 3030337510 ISBN-13(EAN): 9783030337513 Издательство: Springer Рейтинг: Цена: 8104.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed post-conference proceedings for the VLBD conference workshops entitled: Towards Polystores That Manage Multiple Databases, Privacy, Security and/or Policy Issues for Heterogenous Data (Poly 2019) and the Fifth International Workshop on Data Management and Analytics for Medicine and Healthcare (DMAH 2019), held in Los Angeles, CA, USA, in August 2019, in conjunction with the 45th International Conference on Very Large Data Bases, VLDB 2019. The 20 regular papers presented together with 2 keynote papers were carefully reviewed and selected from 31 initial submissions. The papers are organized in topical sections named:Poly 2019: Privacy, Security and/or Policy Issues for Heterogenous Data; Building Polystore Systems.DMAH 2019: Database Enabled Biomedical Research; AI for Healthcare; Knowledge Discovery from Unstructured Biomedical Data; Blockchain and Privacy Preserving Data Management.
Автор: Hsu, Hui-Huang Название: Big Data Analytics for Sensor-Network Collected Intelligence ISBN: 0128093935 ISBN-13(EAN): 9780128093931 Издательство: Elsevier Science Рейтинг: Цена: 15159.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Big Data Analytics for Sensor-Network Collected Intelligence explores state-of-the-art methods for using advanced ICT technologies to perform intelligent analysis on sensor collected data. The book shows how to develop systems that automatically detect natural and human-made events, how to examine people's behaviors, and how to unobtrusively provide better services.
It begins by exploring big data architecture and platforms, covering the cloud computing infrastructure and how data is stored and visualized. The book then explores how big data is processed and managed, the key security and privacy issues involved, and the approaches used to ensure data quality.
In addition, readers will find a thorough examination of big data analytics, analyzing statistical methods for data analytics and data mining, along with a detailed look at big data intelligence, ubiquitous and mobile computing, and designing intelligence system based on context and situation.
Indexing: The books of this series are submitted to EI-Compendex and SCOPUS
Contains contributions from noted scholars in computer science and electrical engineering from around the globe
Provides a broad overview of recent developments in sensor collected intelligence
Edited by a team comprised of leading thinkers in big data analytics
Автор: Tinglong Dai; Sridhar Tayur Название: Handbook of Healthcare Analytics ISBN: 1119300940 ISBN-13(EAN): 9781119300946 Издательство: Wiley Рейтинг: Цена: 17733.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
How can analytics scholars and healthcare professionals access the most exciting and important healthcare topics and tools for the 21st century?
Editors Tinglong Dai (Johns Hopkins) and Sridhar Tayur (Carnegie Mellon), aided by a team of internationally acclaimed experts, have curated this timely volume to help newcomers and seasoned researchers alike to rapidly comprehend a diverse set of thrusts and tools in this rapidly growing cross-disciplinary field. The Handbook covers macro-, meso- and micro-level thrusts, spanning organizational structure, market design, access to (and quality of) care, competing interests, personalized medicine, global health, organ transplantation, healthcare supply chains, ambulatory care, inpatient care, residential care and concierge medicine. No other book in the field matches its scope. It is also the first book to synthesize what has been a highly fragmented research area--an uncoordinated accumulation of papers--and to structure it into a coherent scientific discipline. As Poincare remarked, an accumulation of facts is no more a science than a heap of stones is a house.
The handbook also provides an easy-to-comprehend introduction to five essential research tools--Markov decision process, game theory and information economics, queueing theory (with and without game theory), econometric methods, and data sciences (including machine learning)--by illustrating their uses and applicability on examples from diverse healthcare settings, thus connecting tools with thrusts.
The primary audience of the Handbook includes analytics scholars interested in healthcare and healthcare practitioners interested in analytics. This Handbook:
Instills analytics scholars with a way of thinking that incorporates behavioral, incentive, and policy considerations in various healthcare settings. This change in perspective--a shift in gaze away from narrow, local and one-off operational improvement efforts that do not replicate, scale or remain sustainable--can lead to new knowledge and innovative solutions that healthcare has been seeking so desperately.
Facilitates collaboration between healthcare experts and analytics scholars--to frame and tackle their pressing concerns through appropriate modern mathematical tools designed for this very purpose--ranging from queuing models imbedded with game theory ("queuing games") to game theory methods enhanced by operational considerations ("market design"), from tailored econometric methods to current day data-science methods that include algorithms from machine learning.
While the handbook is designed to be accessible to the independent reader, it may be used in a variety of settings, from a short lecture series on specific topics to a semester-long course covering the entire field.
Автор: Reddy Название: Big Data Analytics ISBN: 3319724126 ISBN-13(EAN): 9783319724126 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed conference proceedings of the 5th International Conference on Big Data Analytics, BDA 2017, held in Hyderabad, India, in December 2017.
Описание: This edited book will serve as a source of reference for technologies and applications for multimodality data analytics in big data environments. After an introduction, the editors organize the book into four main parts on sentiment, affect and emotion analytics for big multimodal data; unsupervised learning strategies for big multimodal data; supervised learning strategies for big multimodal data; and multimodal big data processing and applications. The book will be of value to researchers, professionals and students in engineering and computer science, particularly those engaged with image and speech processing, multimodal information processing, data science, and artificial intelligence.
Автор: Murad Khan; Bilal Jan; Haleem Farman Название: Deep Learning: Convergence to Big Data Analytics ISBN: 9811334587 ISBN-13(EAN): 9789811334580 Издательство: Springer Рейтинг: Цена: 7685.00 р. Наличие на складе: Поставка под заказ.
Описание:
This book presents deep learning techniques, concepts, and algorithms to classify and analyze big data. Further, it offers an introductory level understanding of the new programming languages and tools used to analyze big data in real-time, such as Hadoop, SPARK, and GRAPHX. Big data analytics using traditional techniques face various challenges, such as fast, accurate and efficient processing of big data in real-time. In addition, the Internet of Things is progressively increasing in various fields, like smart cities, smart homes, and e-health. As the enormous number of connected devices generate huge amounts of data every day, we need sophisticated algorithms to deal, organize, and classify this data in less processing time and space. Similarly, existing techniques and algorithms for deep learning in big data field have several advantages thanks to the two main branches of the deep learning, i.e. convolution and deep belief networks. This book offers insights into these techniques and applications based on these two types of deep learning.Further, it helps students, researchers, and newcomers understand big data analytics based on deep learning approaches. It also discusses various machine learning techniques in concatenation with the deep learning paradigm to support high-end data processing, data classifications, and real-time data processing issues. The classification and presentation are kept quite simple to help the readers and students grasp the basics concepts of various deep learning paradigms and frameworks. It mainly focuses on theory rather than the mathematical background of the deep learning concepts. The book consists of 5 chapters, beginning with an introductory explanation of big data and deep learning techniques, followed by integration of big data and deep learning techniques and lastly the future directions.
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