Machine Learning in Medicine - a Complete Overview
The current book is the first publication of a complete overview of machine learning methodologies for the medical and health sector. It was written as a training companion, and as a must-read, not only for physicians and students, but also for any one in
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ine Learning in Medicine a Complete Overview
Machine Learning in Medicine - a Complete Overview
Ton J. Cleophas • Aeilko H. Zwinderman
Machine Learning in Medicine - a Complete Overview With the help from HENNY I. CLEOPHAS-ALLERS, BChem
Ton J. Cleophas Department Medicine Albert Schweitzer Hospital Sliedrecht, The Netherlands
Aeilko H. Zwinderman Department Biostatistics and Epidemiology Academic Medical Center Amsterdam, The Netherlands
Additional material to this book can be downloaded from http://extras.springer.com. ISBN 978-3-319-15194-6 ISBN 978-3-319-15195-3 DOI 10.1007/978-3-319-15195-3
(eBook)
Library of Congress Control Number: 2015930334 Springer Cham Heidelberg New York Dordrecht London © Springer International Publishing Switzerland 2015 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper Springer International Publishing AG Switzerland is part of Springer Science+Business Media (www. springer.com)
Preface
The amount of data stored in the world’s databases doubles every 20 months, as estimated by Usama Fayyad, one of the founders of machine learning and co-author of the book Advances in Knowledge Discovery and Data Mining (ed. by the American Association for Artificial Intelligence, Menlo Park, CA, USA, 1996), and clinicians, familiar with traditional statistical methods, are at a loss to analyze them. Traditional methods have, indeed, difficulty to identify outliers in large datasets, and to find patterns in big data and data with multiple exposure/outcome variables. In addition, analysis-rules for surveys and questionnaires, which are currently common methods of data collection, are, essentially, missing. Fortunately, the new discipline, machine learning, is able to cover all of these limitations. So far, medical professionals have been rather reluctant to use machine learning. Ravinda Khattree, co-author of the book Computational Methods in Biomedical Research (ed. by Chapman & Hall, Baton Rouge, LA, USA, 2007) suggests that
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