Demystifying Big Data and Machine Learning for Healthcare

Nonfiction, Health & Well Being, Medical, Reference, Hospital Administration & Care, Administration, Business & Finance, Industries & Professions, Industries
Cover of the book Demystifying Big Data and Machine Learning for Healthcare by Prashant Natarajan, John C. Frenzel, Detlev H. Smaltz, Taylor and Francis
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Prashant Natarajan, John C. Frenzel, Detlev H. Smaltz ISBN: 9781315389301
Publisher: Taylor and Francis Publication: February 15, 2017
Imprint: CRC Press Language: English
Author: Prashant Natarajan, John C. Frenzel, Detlev H. Smaltz
ISBN: 9781315389301
Publisher: Taylor and Francis
Publication: February 15, 2017
Imprint: CRC Press
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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:

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.

More books from Taylor and Francis

Cover of the book Foundations of Social Work Practice with Lesbian and Gay Persons by Prashant Natarajan, John C. Frenzel, Detlev H. Smaltz
Cover of the book Management Consultancy by Prashant Natarajan, John C. Frenzel, Detlev H. Smaltz
Cover of the book Countering the Financing of Terrorism by Prashant Natarajan, John C. Frenzel, Detlev H. Smaltz
Cover of the book Heidegger and French Philosophy by Prashant Natarajan, John C. Frenzel, Detlev H. Smaltz
Cover of the book Routledge Handbook of Landscape Character Assessment by Prashant Natarajan, John C. Frenzel, Detlev H. Smaltz
Cover of the book Recruitment and Selection Revised Edition by Prashant Natarajan, John C. Frenzel, Detlev H. Smaltz
Cover of the book 3ds Max Design Architectural Visualization by Prashant Natarajan, John C. Frenzel, Detlev H. Smaltz
Cover of the book Cultural Politics in Modern India by Prashant Natarajan, John C. Frenzel, Detlev H. Smaltz
Cover of the book Social Justice in Globalized Fitness and Health by Prashant Natarajan, John C. Frenzel, Detlev H. Smaltz
Cover of the book International Money and Finance by Prashant Natarajan, John C. Frenzel, Detlev H. Smaltz
Cover of the book The Imagination by Prashant Natarajan, John C. Frenzel, Detlev H. Smaltz
Cover of the book Chronic Fatigue Syndrome by Prashant Natarajan, John C. Frenzel, Detlev H. Smaltz
Cover of the book Organisational Capital by Prashant Natarajan, John C. Frenzel, Detlev H. Smaltz
Cover of the book Special Education and Social Control by Prashant Natarajan, John C. Frenzel, Detlev H. Smaltz
Cover of the book The Rise of Corporate Publishing and Its Effects on Authorship in Early Twentieth Century America by Prashant Natarajan, John C. Frenzel, Detlev H. Smaltz
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy