Demystifying Big Data and Machine Learning for Healthcare

Prashant Natarajan

ISBN 10: 1032097167 / ISBN 13: 9781032097169

Published by CRC Press, 2021

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - 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 mythsBecome an expert in separating hype from realityUnderstand the V's that matter in healthcare and whyHarmonize the 4 C's across little and big dataChoose data fi delity over data qualityLearn how to apply the NRF FrameworkMaster applied machine learning for healthcareConduct a guided tour of learning algorithmsRecognize 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.

Seller Inventory # 9781032097169

Bibliographic Details

• Title: Demystifying Big Data and Machine Learning for Healthcare
• Publisher: CRC Press
• Publication Date: 2021
• Language: English
• ISBN 10: 1032097167
• ISBN 13: 9781032097169
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 254W x 178H x 12D millimeters 254 millimeters width by 178 millimeters height by 12 millimeters depth
• Weight: 405 grams

About this title

Synopsis

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.

About the Author

Prashant Natarajan, John C. Frenzel, Detlev H. Smaltz