Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics Books

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Demystifying Big Data Machine Learning and Deep Learning for Healthcare Analytics


Demystifying Big Data  Machine Learning  and Deep Learning for Healthcare Analytics
  • Author : Pradeep Nijalingappa
  • Publisher : Academic Press
  • Release : 2021-06-15
  • ISBN : 0128216336
  • Language : En, Es, Fr & De
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Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing the world of data utilization, especially in clinical healthcare. Various techniques, methodologies, algorithms are presented in this book by researchers to organize data in a structured manner that will assist physicians in the precision care of patients and help biomedical engineeris and computers scientists understand the impact of these techniques on healthcare analytics. The book is divided into two Parts. Part 1 covers the Big Data aspects, i.e., 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 as well. 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, and healthcare researchers and clinicians. Provides a comprehensive reference for biomedical engineers, computer sciences, advanced industry practitioners, researchers, and clinicians to understand and develop healthcare analytics using advanced tools and technologies Includes in-depth illustration 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 implementations

Demystifying Big Data and Machine Learning for Healthcare


Demystifying Big Data and Machine Learning for Healthcare
  • Author : Prashant Natarajan
  • Publisher : CRC Press
  • Release : 2017-02-15
  • ISBN : 9781315389301
  • Language : En, Es, Fr & De
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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.

Machine Learning with Health Care Perspective


Machine Learning with Health Care Perspective
  • Author : Vishal Jain
  • Publisher : Springer Nature
  • Release : 2020-03-09
  • ISBN : 9783030408503
  • Language : En, Es, Fr & De
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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


Intelligence Based Medicine
  • Author : Anthony C. Chang
  • Publisher : Academic Press
  • Release : 2020-06-27
  • ISBN : 9780128233382
  • Language : En, Es, Fr & De
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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

A Global Approach to Data Value Maximization


A Global Approach to Data Value Maximization
  • Author : Paolo Dell’Aversana
  • Publisher : Cambridge Scholars Publishing
  • Release : 2019-04-17
  • ISBN : 9781527533370
  • Language : En, Es, Fr & De
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This book presents a systematic discussion about methods and techniques used to extract the maximum informative value from complex data sets. A multitude of approaches and techniques can be applied for that purpose, including data fusion and model integration, multimodal data analysis in different physical domains, audio-video display of data through techniques of “sonification”, multimedia machine learning, and hybrid methods of data analysis. The book begins with the domain of geosciences, before moving on to other scientific areas, like diagnostic medicine and various engineering sectors. As such, it will appeal to a large audience, including geologists and geophysicists, data scientists, physicians and cognitive scientists, and experts in social sciences and knowledge management.