Fundamentals of Clinical Data Science
This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (bi
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Fundamentals of Clinical Data Science
Pieter Kubben · Michel Dumontier Andre Dekker Editors
Fundamentals of Clinical Data Science
Editors Pieter Kubben Department of Neurosurgery Maastricht University Maastricht, Limburg The Netherlands
Michel Dumontier Institute of Data Science Maastricht University Maastricht, Limburg The Netherlands
Andre Dekker Maastro Clinic Maastricht, Limburg The Netherlands
This book is an open access publication. ISBN 978-3-319-99712-4 ISBN 978-3-319-99713-1 (eBook) https://doi.org/10.1007/978-3-319-99713-1 Library of Congress Control Number: 2018963226 © The Editor(s) (if applicable) and The Author(s) 2019 Open Access This book is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this book are included in the book’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. 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. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Introduction “Fundamentals of Clinical Data Science”
In the era of eHealth and personalized medicine, “big data” and “machine learning” are increasingly becoming part of the medical world. Algorithms are capable of supporting diagnostic and therapeutic processes and offer added value for both healthcare professionals and patients. The field of big data, machine learning, deep learning, and algorithm development and validation is often referred to as “data science,” and “data scientist” was mentioned in Harvard Business Review as “the sexiest job of the 21th century” (https://hbr.org/2012/10/data-scientist-the-sexiestjob-of-the-21st-century). A commonly u
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