Demystifying Big Data Machine Learning and Deep Learning for Healthcare Analytics

Various techniques, methodologies, and algorithms are presented in this book to organize data in a structured manner that will assist physicians in the care of patients and help biomedical engineers and computer scientists understand the ...

Author: Pradeep N

Publisher: Elsevier

ISBN: 9780128216330

Category: Science

Page: 372

View: 720

DOWNLOAD →

Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization, especially in clinical healthcare. Various techniques, methodologies, and algorithms are presented in this book to organize data in a structured manner that will assist physicians in the care of patients and help biomedical engineers and computer scientists understand the impact of these techniques on healthcare analytics. The book is divided into two parts: Part 1 covers big data aspects such as healthcare decision support systems and analytics-related topics. Part 2 focuses on the current frameworks and applications of deep learning and machine learning, and provides an outlook on future directions of research and development. The entire book takes a case study approach, providing a wealth of real-world case studies in the application chapters to act as a foundational reference for biomedical engineers, computer scientists, healthcare researchers, and clinicians. Provides a comprehensive reference for biomedical engineers, computer scientists, advanced industry practitioners, researchers, and clinicians to understand and develop healthcare analytics using advanced tools and technologies Includes in-depth illustrations of advanced techniques via dataset samples, statistical tables, and graphs with algorithms and computational methods for developing new applications in healthcare informatics Unique case study approach provides readers with insights for practical clinical implementation

Demystifying Big Data Machine Learning and Deep Learning for Healthcare Analytics

and Deep Learning for Healthcare Analytics Edited by Pradeep N , Sandeep Kautish and Sheng - Lung Peng Examines the latest techniques of big data , machine learning , and deep learning as applied to healthcare analytics , using a case ...

Author: Pradeep N

Publisher: Academic Press

ISBN: 9780128220443

Category: Science

Page: 372

View: 320

DOWNLOAD →

Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization, especially in clinical healthcare. Various techniques, methodologies, and algorithms are presented in this book to organize data in a structured manner that will assist physicians in the care of patients and help biomedical engineers and computer scientists understand the impact of these techniques on healthcare analytics. The book is divided into two parts: Part 1 covers big data aspects such as healthcare decision support systems and analytics-related topics. Part 2 focuses on the current frameworks and applications of deep learning and machine learning, and provides an outlook on future directions of research and development. The entire book takes a case study approach, providing a wealth of real-world case studies in the application chapters to act as a foundational reference for biomedical engineers, computer scientists, healthcare researchers, and clinicians. Provides a comprehensive reference for biomedical engineers, computer scientists, advanced industry practitioners, researchers, and clinicians to understand and develop healthcare analytics using advanced tools and technologies Includes in-depth illustrations of advanced techniques via dataset samples, statistical tables, and graphs with algorithms and computational methods for developing new applications in healthcare informatics Unique case study approach provides readers with insights for practical clinical implementation

Demystifying Big Data and Machine Learning for Healthcare

Chapter 1 provides an introduction, makes the case for big data in healthcare, reviews evolutionary analytics, and creates a rich tapestry of high-value information sources that may be linked to an individual for healthcare.

Author: Prashant Natarajan

Publisher: CRC Press

ISBN: 9781315389301

Category: Medical

Page: 233

View: 645

DOWNLOAD →

Healthcare transformation requires us to continually look at new and better ways to manage insights – both within and outside the organization today. Increasingly, the ability to glean and operationalize new insights efficiently as a byproduct of an organization’s day-to-day operations is becoming vital to hospitals and health systems ability to survive and prosper. One of the long-standing challenges in healthcare informatics has been the ability to deal with the sheer variety and volume of disparate healthcare data and the increasing need to derive veracity and value out of it. Demystifying Big Data and Machine Learning for Healthcare investigates how healthcare organizations can leverage this tapestry of big data to discover new business value, use cases, and knowledge as well as how big data can be woven into pre-existing business intelligence and analytics efforts. This book focuses on teaching you how to: Develop skills needed to identify and demolish big-data myths Become an expert in separating hype from reality Understand the V’s that matter in healthcare and why Harmonize the 4 C’s across little and big data Choose data fi delity over data quality Learn how to apply the NRF Framework Master applied machine learning for healthcare Conduct a guided tour of learning algorithms Recognize and be prepared for the future of artificial intelligence in healthcare via best practices, feedback loops, and contextually intelligent agents (CIAs) The variety of data in healthcare spans multiple business workflows, formats (structured, un-, and semi-structured), integration at point of care/need, and integration with existing knowledge. In order to deal with these realities, the authors propose new approaches to creating a knowledge-driven learning organization-based on new and existing strategies, methods and technologies. This book will address the long-standing challenges in healthcare informatics and provide pragmatic recommendations on how to deal with them.

Computational Intelligence in Robotics and Automation

Challenges and Opportunities of Big Data Integration in PatientCentric Healthcare Analytics Using Mobile Networks. In Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics (pp. 85–108). Academic Press. 43.

Author: S.S Nandhini

Publisher: CRC Press

ISBN: 9781000686517

Category: Technology & Engineering

Page: 284

View: 890

DOWNLOAD →

This book will help readers to understand the concepts of computational intelligence in automation industries, industrial IoT (IIOT), cognitive systems, data science, and Ecommerce real time applications. The book: Covers computational intelligence in automation industries, industrial IoT (IIOT) , cognitive systems and medical Imaging Discusses intelligent robotics applications with the integration of automation and artificial intelligence Covers foundations of the mathematical concepts applied in robotics and industry automation applications Provides application of artificial intelligence (AI) in the area of computational intelligence The text covers important topics including computational intelligence mathematical modeling, cognitive manufacturing in industry 4.0, artificial intelligence algorithms in robot development, collaborative robots and industrial IoT (IIoT), medical imaging, and multi-robot systems. The text will be useful for graduate students, professional and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer science. Discussing the advantages of the integrated platform of industry automation, robotics and computational intelligence, this text will be useful for graduate students, professional and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer science. It enlightens the foundations of the mathematical concepts applied in robotics and industry automation applications.

Digital Health Transformation with Blockchain and Artificial Intelligence

Smart healthcare systems using big data, Elsevier: Demystifying big data, machine learning and deep learning for healthcare analytics, Ch. 2, 1–16. doi:10.1016/B978-0-12-821633-0.00002-7 14. Collier, R. 2017.

Author: Chinmay Chakraborty

Publisher: CRC Press

ISBN: 9781000580945

Category: Technology & Engineering

Page: 368

View: 314

DOWNLOAD →

The book Digital Health Transformation with Blockchain and Artificial Intelligence covers the global digital revolution in the field of healthcare sector. The population has been overcoming the COVID-19 period; therefore, we need to establish intelligent digital healthcare systems using various emerging technologies like Blockchain and Artificial Intelligence. Internet of Medical Things is the technological revolution that has included the element of "smartness" in the healthcare industry and also identifying, monitoring, and informing service providers about the patient’s clinical information with faster delivery of care services. This book highlights the important issues i.e. (a) How Internet of things can be integrated with the healthcare ecosystem for better diagnostics, monitoring, and treatment of the patients, (b) Artificial Intelligence for predictive and preventive healthcare systems, (c) Blockchain for managing healthcare data to provide transparency, security, and distributed storage, and (d) Effective remote diagnostics and telemedicine approach for developing smart care. The book encompasses chapters belong to the blockchain, Artificial Intelligence, and Big health data technologies. Features: Blockchain and internet of things in healthcare systems Secure Digital Health Data Management in Internet of Things Public Perception towards AI-Driven Healthcare Security, privacy issues and challenges in adoption of smart digital healthcare Big data analytics and Internet of things in the pandemic era Clinical challenges for digital health revolution Artificial intelligence for advanced healthcare Future Trajectory of Healthcare with Artificial Intelligence 9 Parkinson disease pre-diagnosis using smart technologies Emerging technologies to combat the COVID-19 Machine Learning and Internet of Things in Digital Health Transformation Effective Remote Healthcare and Telemedicine Approaches Legal implication of blockchain technology in public health This Book on "Digital Health Transformation with Blockchain and Artificial Intelligence" aims at promoting and facilitating exchanges of research knowledge and findings across different disciplines on the design and investigation of secured healthcare data analytics. It can also be used as a textbook for a Masters course in security and biomedical engineering. This book will also present new methods for the medical data analytics, blockchain technology, and diagnosis of different diseases to improve the quality of life in general, and better integration into digital healthcare.

Emerging Technologies in Healthcare

[33] Y. W. Chen and L. C. Jain (eds.), Deep Learning in Healthcare: Paradigms and Applications. Springer, 2019. [34] P. Natarajan, J. C. Frenzel, and D. H. Smaltz, Demystifying Big Data and Machine Learning for Healthcare.

Author: Matthew N. O. Sadiku

Publisher: AuthorHouse

ISBN: 9781665528429

Category: Medical

Page: 316

View: 120

DOWNLOAD →

Health is regarded as one of the global challenges for mankind. Healthcare is a complex system that covers processes of diagnosis, treatment, and prevention of diseases. It constitutes a fundamental pillar of the modern society. Modern healthcare is technological healthcare. Technology is everywhere. This book focuses on twenty-one emerging technologies in the healthcare industry. An emerging technology is one that holds the promise of creating a new economic engine and is trans-industrial. Emerging technological trends are rapidly transforming businesses in general and healthcare in particular in ways that we find hard to imagine. Artificial intelligence (AI), machine learning, robots, blockchain, cloud computing, Internet of things (IoT), and augmented & virtual reality are some of the technologies at the heart of this revolution and are covered in this book. The convergence of these technologies is upon us and will have a huge impact on the patient experience

Demystifying AI for the Enterprise

“Amazon Scraps Secret AI Recruiting Tool That Showed Bias Against Women.” ed. ... “A Tale of 2 T's: When Analytics and Artificial Intelligence Go Bad: Healthcare It Today. ... Demystifying Big Data and Machine Learning for Healthcare.

Author: Prashant Natarajan

Publisher: CRC Press

ISBN: 9781351032926

Category: Computers

Page: 434

View: 769

DOWNLOAD →

Artificial intelligence (AI) in its various forms –– machine learning, chatbots, robots, agents, etc. –– is increasingly being seen as a core component of enterprise business workflow and information management systems. The current promise and hype around AI are being driven by software vendors, academic research projects, and startups. However, we posit that the greatest promise and potential for AI lies in the enterprise with its applications touching all organizational facets. With increasing business process and workflow maturity, coupled with recent trends in cloud computing, datafication, IoT, cybersecurity, and advanced analytics, there is an understanding that the challenges of tomorrow cannot be solely addressed by today’s people, processes, and products. There is still considerable mystery, hype, and fear about AI in today’s world. A considerable amount of current discourse focuses on a dystopian future that could adversely affect humanity. Such opinions, with understandable fear of the unknown, don’t consider the history of human innovation, the current state of business and technology, or the primarily augmentative nature of tomorrow’s AI. This book demystifies AI for the enterprise. It takes readers from the basics (definitions, state-of-the-art, etc.) to a multi-industry journey, and concludes with expert advice on everything an organization must do to succeed. Along the way, we debunk myths, provide practical pointers, and include best practices with applicable vignettes. AI brings to enterprise the capabilities that promise new ways by which professionals can address both mundane and interesting challenges more efficiently, effectively, and collaboratively (with humans). The opportunity for tomorrow’s enterprise is to augment existing teams and resources with the power of AI in order to gain competitive advantage, discover new business models, establish or optimize new revenues, and achieve better customer and user satisfaction.

Intelligent Healthcare

Supporting private data on hyperledger fabric with secure multiparty computation. IBM Journal of Research and Development, 63(2/3), ... Elsevier: Demystifying Big data, Machine learning and Deep learning for Healthcare Analytics, 2, ...

Author: Chinmay Chakraborty

Publisher: Springer Nature

ISBN: 9789811681509

Category: Artificial intelligence

Page: 493

View: 578

DOWNLOAD →

The book Intelligent Healthcare: Infrastructure, Algorithms, and Management® cover a wide range of research topics on innovative intelligent healthcare solutions and advancements with the latest research developments. Data analytics are relevant for healthcare to meet many technical challenges and issues that need to be addressed to realize this potential. The advanced healthcare systems have to be upgraded with new capabilities such as data analytics, machine learning, intelligent decision making, and more professional services. The Internet of Things helps to design and develop intelligent healthcare solutions assisted by security, data analytics, and machine learning. This book will provide federated learning, Data-driven infrastructure design, analytical approaches, and technological solutions with case studies for smart healthcare. This book aims to attract works on multidisciplinary research spanning across computer science and engineering, environmental studies, services, urban planning and development, Healthcare, social sciences, and industrial engineering on technologies, case studies, novel approaches, and visionary ideas related to data-driven innovative learning and computing solutions and big medical data-powered applications to cope with the real-world challenges for building smart healthcare sectors. Main Features: Ø Immersive technologies in healthcare Ø Internet of medical things Ø Federated learning algorithms Ø Explainable AI in Pervasive Healthcare Ø New management principles using biomedical data Ø Secured healthcare management systems This book aims to set up a better understanding of data scientists, researchers, and technologists under innovative digital health. The reader can find out existing research challenges, current market trends, and low-cost technologies to smoothly address the digital health issue.

Machine Learning with Health Care Perspective

Machine Learning and Healthcare Vishal Jain, Jyotir Moy Chatterjee ... Another edited book entitled “Demystifying Big Data, Machine Learning and Deep Learning for Health Care Analytics” is in progress and to be published by Elsevier ...

Author: Vishal Jain

Publisher: Springer Nature

ISBN: 9783030408503

Category: Technology & Engineering

Page: 415

View: 479

DOWNLOAD →

This unique book introduces a variety of techniques designed to represent, enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. Providing a unique compendium of current and emerging machine learning paradigms for healthcare informatics, it reflects the diversity, complexity, and the depth and breadth of this multi-disciplinary area. Further, it describes techniques for applying machine learning within organizations and explains how to evaluate the efficacy, suitability, and efficiency of such applications. Featuring illustrative case studies, including how chronic disease is being redefined through patient-led data learning, the book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare challenges.

Intelligence Based Medicine

—A comprehensive and current textbook on medical applications of AI with more emphasis on the computer and data science aspects. ... Advanced data analytics in health. ... Demystifying big data and machine learning for health care.

Author: Anthony C. Chang

Publisher: Academic Press

ISBN: 9780128233382

Category: Medical

Page: 534

View: 234

DOWNLOAD →

Intelligence-Based Medicine: Data Science, Artificial Intelligence, and Human Cognition in Clinical Medicine and Healthcare provides a multidisciplinary and comprehensive survey of artificial intelligence concepts and methodologies with real life applications in healthcare and medicine. Authored by a senior physician-data scientist, the book presents an intellectual and academic interface between the medical and the data science domains that is symmetric and balanced. The content consists of basic concepts of artificial intelligence and its real-life applications in a myriad of medical areas as well as medical and surgical subspecialties. It brings section summaries to emphasize key concepts delineated in each section; mini-topics authored by world-renowned experts in the respective key areas for their personal perspective; and a compendium of practical resources, such as glossary, references, best articles, and top companies. The goal of the book is to inspire clinicians to embrace the artificial intelligence methodologies as well as to educate data scientists about the medical ecosystem, in order to create a transformational paradigm for healthcare and medicine by using this emerging new technology. Covers a wide range of relevant topics from cloud computing, intelligent agents, to deep reinforcement learning and internet of everything Presents the concepts of artificial intelligence and its applications in an easy-to-understand format accessible to clinicians and data scientists Discusses how artificial intelligence can be utilized in a myriad of subspecialties and imagined of the future Delineates the necessary elements for successful implementation of artificial intelligence in medicine and healthcare