Demystifying Big Data and Machine Learning for Healthcare Advance Reviews Data is quickly emerging as the greatest asset of the healthcare industry. Th e trend in our industry is to drive many decisions supported by data. Th e authors have done a great job put- ting together the issues, challenges and benefi ts of adopting a long view of Big Data. It is a walk of maturity with the real gold nuggets coming in Analytics 3.0 and beyond. Th is will not be solved with a product or purchased off the shelf. Big Data needs to be part of the DNA of an organization. Th anks to the authors for putting this together for us.  —Chris Belmont, MBA Vice President and Chief Information Offi cer MD Anderson Cancer Center Intelligent decisions are best made with data that gives us rich context and a fuller view of all parameters and possibilities. However, how do we not drown in all this data we’re generating? How do we stay afl oat, swim, and surf—harnessing the tremendous power of this valuable resource? Th e authors attempt successfully to separate myth from reality with regard to the potential for big data and machine learning in healthcare. Great read! —Rasu B. Shrestha, MD, MBA Chief Innovation Offi cer, UPMC Executive Vice President, UPMC Enterprises Th is book is a must-read for any provider of healthcare services interested in practical recommen- dations and best practices about leveraging big data in its many ways and formats. Th e authors draw on their extensive practical experience to separate myths from realities and provide useful insights into the handling of the related challenges through the usage of real-world case studies. —Prof. Dr. Diego Kuonen CEO and CAO, Statoo Consulting, Switzerland Professor of Data Science, University of Geneva, Switzerland iii iv Demystifying Big Data and Machine Learning for Healthcare Big data has become so ubiquitous a term that its use conveys very little of value, particularly in healthcare. For those who want to actually understand this exciting area in meaningful way and how in turn it can add considerable value to their organisation’s success, I would strongly recommend this book. —Jonathan Sheldon, PhD Global Vice President, Healthcare Oracle Health Sciences As the leader of analytics at a large national IDN of primarily community hospitals, our “small data” analytic needs alone can seem overwhelming at times. At the same time, we are seeing the greater value of advanced analytics and beginning to realize the promise of machine learning pre- dictive algorithms. We are admittedly just beginning our journey into true “big-data” use cases, and I found this book to be an extremely useful overview of big-data and machine-learning ana- lytic techniques and applications in healthcare. Th e book is written in an engaging format with simple defi nitions and descriptions leading to real-world applications. I recommend this book for healthcare leaders interested in a book that cuts through the hype of big data and eff ectively com- piles the vast landscape of big-data analytic topics and terminologies into a single, practical volume. —Nick T. Scartz Corporate Chief Analytics Offi cer, Adventist Health System Payers—not the least of which are Medicare and Medicaid—are demanding better value. Stuck with one foot in the past, policy makers think we might cut our way out of spiraling health costs. At the same time, Congress and the Administration are aggressively moving to at-risk, value- based models. Reimbursement economics has never been quite so precarious. It is into this environment the authors insert important new insights, with key takeaways and action steps. Big data and machine learning are transforming how real-world evidence is collected and leveraged to enable data-driven transparency, collaboration, and improved patient outcomes. Written for a broad audience of healthcare stakeholders, this “fi rst of its kind” book off ers illuminating strategies, concepts, and best practices. Th e authors write in remarkably jargon-free language that makes this book an engaging and thought-provoking read for non- technical—and technical—readers alike. Highly recommended for those seeking not just to stay afl oat, but to operationalize strategies and succeed in the new world of value-based care. —Joel White President, Council for Aff ordable Health Coverage Co-author, Facts and Figures on Government Finance Th is is the book that fi nally brings together in one volume the defi nitions and tools to understand big data, AI, and machine learning for the busy clinician, hospital administrator, or policy maker without requiring them to go back to school and take a graduate-level course curriculum to learn. —Oscar Streeter, Jr., MD, FACRO Chief Medical & Scientifi c Offi cer, CA Division of American Cancer Society Radiation Oncologist, Th e Center for Th ermal Oncology Advance Reviews v Th e discipline of managing and analyzing big data continues to evolve at a rapid pace. Th e authors do a solid job of recognizing this growing complexity and off ering an accessible introduction to the discipline in its increasing breadth. Presented is a discussion of both the art and science of big data; including the diff erent sub-classes of big-data management and analysis, approaches to solve each challenge, and how these challenges map to healthcare problems of importance. Highly recom- mended for healthcare leaders interested in data-intensive advances for care delivery. —Zeeshan Syed, MD, PhD Director, Clinical Inference and Algorithms Program, Stanford Health Care Clinical Associate Professor, Stanford University School of Medicine It’s my belief that the next dimension of clinical research and precision medicine will be built off the ability to not only access the vast amounts of data available but to be able to identify and quickly assess the valuable insights buried within. Th is book does a great job providing the perspective needed in practical terms to understand how far we’ve come and where we are in the access to and use of big data. It’s a must read for those from health-care providers to data scien- tists looking to understand the tremendous potential and practical applications that machine learning and advanced analytics can and will bring. We have truly come to a point where health care has exceeded the capabilities of an individual person and must be augmented with machines that allow us to understand and apply health care appropriately. I strongly recom- mend this book to those who plan to be part of the analytically driven health-care environment. —Matt Gross Chief Solutions Offi cer, Duke Clinical Research Institute Former Director of the Health and Life Sciences Global Practice, SAS A comprehensive, timely, and truly invigorating book about machine learning, artifi cial intel- ligence, and big data in health care. Th ese are topics that are shaping research and the practice of medicine—today. Th e authors show us the promise and potential pitfalls of this important topic and how this information will shape our future. Enjoyable and understandable—you will not need an MD or degree in computer science to gain a deep understanding of the future of big data and AI in healthcare. Th e handy “Common Uses in Healthcare” sections in Chapter 7 drive home the saliency of the topics. A must read for health-care providers and patients alike. —Phillip J. Beron, MD Assistant Professor, Department of Radiation Oncology David Geff en School of Medicine at UCLA Th is is a deep dive into Big Data and Machine Learning for healthcare, yet these complex and challenging topics are made clear and comprehensible in this engaging exposition. It is a must-read for those who wish to understand these dominant forces that are rapidly reshaping medicine and to learn how best to apply them to their own healthcare enterprise. —Pratik Mukherjee, MD, PhD Professor, UCSF School of Medicine vi Demystifying Big Data and Machine Learning for Healthcare Learning algorithms are as essential for extracting information from big data as a transmission is for a car to leverage the horsepower created by an engine. Th ese algorithms have the poten- tial for revolutionizing the healthcare industry by identifying new patterns and information in data, continuously adjusting themselves to changes, automating many aspects of big-data analysis, and operationalizing information extracted from big data. Th is book demystifi es machine learning algorithms by providing a solid overview of the state of the art and by presenting the relationship of big data and machine learning algorithms. Th e authors describe how machine learning can be used in the healthcare industry to support physicians’ decisions, to improve the quality of care, and to detect new trends in healthcare data. Th is book is a must-read for healthcare professionals who are entering the new world of big-data analysis.  —Michael Sassin, Dr. techn. Director of Software Development, GBU Architecture Oracle Th e future of healthcare will be built on the back of data. Organizations that don’t acknowl- edge and prepare for this are going to be left behind. Th is book serves as the perfect foundation for organizations that want to make sure they’re prepared for that data-driven future. What makes this book particularly excellent is that it does a great job acknowledging past experiences and infrastructure, providing practical applications of what can be done today, and then looks to the future of where big data and machine learning are headed. Th e cherry on top are the case studies where they show how the concepts are working in actual healthcare situations. —John Lynn (@techguy) Founder of HealthcareScene.com Global healthcare challenges and trends such as aging populations, increasing costs, patient engagement, ubiquitous devices and sensors, personalized medicine, changing reimbursement and economic models demand a new approach to informatics. Th is approaching tsunami of data, including clinical, fi nancial, genomic, wearable, and device- and patient-generated data will overwhelm any clinician, patient, or researcher, as well as any traditional decision-making system. Machine learning and AI are going to be critical components of these new models, and this book will serve as a solid foundation for anyone attempting to rise to this challenge. —Steve Jepsen Vice President, Healthcare Integration Services Global HealthSuite Lab Lead Healthcare Transformation Services, Philips Demystifying Big Data and Machine Learning for Healthcare Prashant Natarajan • John C. Frenzel • Detlev H. Smaltz CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2017 by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S. Government works Printed on acid-free paper Version Date: 20170112 International Standard Book Number-13: 978-1-138-03263-7 (Hardback) This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the valid- ity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or uti- lized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopy- ing, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, please access www.copyright.com (http:// www.copyright.com/) or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. CCC is a not-for-profit organization that provides licenses and registration for a variety of users. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Visit the Taylor & Francis Web site at http://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com Dedications To my wonderful parents, Saraswati and Dr. V.N. Iyer, my loving wife, Vishnu, and my darling daughter, Shivani. —Prashant Natarajan To Elizabeth and Alexandra, my two beauties. —John Frenzel To my wife Sandy, for her patience, love, and support throughout my career. —Herb Smaltz To Tenne, my wonderful wife and dedicated, loving mother of our four amazing sons, who tragically died during the writing of this book. She will always be my inspiration. —Bob Rogers ix