Demystifying big data and machine learning for healthcare / Prashant Natarajan, John C. Frenzel, Detlev H. Smaltz.

"HIMSS."

"A Productivity Press book."

Includes bibliographical references and index.

Introduction / Herb Smaltz -- Healthcare and the Big Data V's / Prashant Natarajan -- Big Data: How to Get Started / John Frenzel -- Big Data: Challenges / John Frenzel -- Best Practices: Separating Myth from Realty / Prashant Natarajan -- Big Data Advanced Topics / John Frenzel and Herb Smaltz -- Applied Machine Learning for Healthcare / Prashant Natarajan and Bob Rogers -- Introduction to Case Studies / Prashant Natarajan -- Penn Medicine: Precision Medicine and Big Data / Brian Wells -- Ascension: Our Advanced Analytics Journey / Tony Byram -- University of Texas MD Anderson: Streaming Analytics / John Frenzel -- US Health Insurance Organization: Financial Reporting Analytics with Big Data / Marc Perlman, Larry Manno, and Shalin Saini -- CIAPM: California Initiative to Advance Precision Medicine / Elizabeth Baca -- University of California San Francisco: AI for Imaging of Neurological Emergencies / Pratik Mukherjee -- BayCare Health System: Actionable, Agile Analytics Using Data Variety / Apparsamy (Balaji) Balaji -- Arterys: Deep Learning for Medical Imaging / Carla Leibowitz.

"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."--Back cover.