Demystifying Big Data and Machine Learning for Healthcare eBook
$25.00
Healthcare transformation requires us to continually look at new and better ways to manage insights – both within and outside the organization today.
Digital edition, delivered to your inbox within 24 hours of payment. No shipping, no delivery charge.
About this eTextbook
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. ISBNs for Demystifying Big Data and Machine Learning for Healthcare are 9781315389301, 1315389304 and the print ISBNs are 9780367828080, 0367828081. Additional ISBNs include 9781138032637, 9781315389325, 9781138055209, 9781032097169, 9781315389295, 9781032324494, 9781315389318.
Publication details
- Author: Prashant Natarajan; John C. Frenzel; Detlev H. Smaltz
- Publisher: CRC Press
- Additional ISBN: 9781138032637, 9781315389325, 9781138055209, 9781032097169, 9781315389295, 9781032324494, 9781315389318
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About this eTextbook
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. ISBNs for Demystifying Big Data and Machine Learning for Healthcare are 9781315389301, 1315389304 and the print ISBNs are 9780367828080, 0367828081. Additional ISBNs include 9781138032637, 9781315389325, 9781138055209, 9781032097169, 9781315389295, 9781032324494, 9781315389318.
Publication details
- Author: Prashant Natarajan; John C. Frenzel; Detlev H. Smaltz
- Publisher: CRC Press
- Additional ISBN: 9781138032637, 9781315389325, 9781138055209, 9781032097169, 9781315389295, 9781032324494, 9781315389318
What you receive
- A licensed digital edition of this title, supplied as PDF or EPUB. The format is set by the publisher, so email us the ISBN before ordering and we will confirm which one it is.
- Delivered to the email address on your order as a secure download link, usually within minutes and always within 24 hours of payment.
- Download links do not expire and there is no cap on re-downloads. Readable on any computer, tablet or phone, online or offline.
- This is a digital edition. Nothing is printed, packed or posted, and there is no delivery charge.
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