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Demystifying Big Data and Machine Learning for Healthcare (Hardcover)
Loot Price: R2,314
Discovery Miles 23 140
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Demystifying Big Data and Machine Learning for Healthcare (Hardcover)
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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.
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