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Table of contents

  1. Front Matter
    Pages i-viii
  2. Data Collection

    1. Front Matter
      Pages 1-1
    2. Pieter Kubben
      Pages 3-9 Open Access
    3. Alberto Traverso, Frank J. W. M. Dankers, Leonard Wee, Sander M. J. van Kuijk
      Pages 11-17 Open Access
    4. Stefan Schulz, Robert Stegwee, Catherine Chronaki
      Pages 19-36 Open Access
    5. Paula Jansen, Linda van den Berg, Petra van Overveld, Jan-Willem Boiten
      Pages 37-53 Open Access
    6. Christopher F. Mondschein, Cosimo Monda
      Pages 55-71 Open Access
  3. From Data to Model

    1. Front Matter
      Pages 73-73
    2. Sander M. J. van Kuijk, Frank J. W. M. Dankers, Alberto Traverso, Leonard Wee
      Pages 75-84 Open Access
    3. Christian Herff, Dean J. Krusienski
      Pages 85-100 Open Access
    4. Frank J. W. M. Dankers, Alberto Traverso, Leonard Wee, Sander M. J. van Kuijk
      Pages 101-120 Open Access
    5. Alberto Traverso, Frank J. W. M. Dankers, Biche Osong, Leonard Wee, Sander M. J. van Kuijk
      Pages 121-133 Open Access
    6. Leonard Wee, Sander M. J. van Kuijk, Frank J. W. M. Dankers, Alberto Traverso, Mattea Welch, Andre Dekker
      Pages 135-150 Open Access
  4. From Model to Application

    1. Front Matter
      Pages 151-151
    2. A. T. M. Wasylewicz, A. M. J. W. Scheepers-Hoeks
      Pages 153-169 Open Access
    3. Pieter Kubben
      Pages 171-179 Open Access
    4. Henri J. Boersma, Tiffany I. Leung, Rob Vanwersch, Elske Heeren, G. G. van Merode
      Pages 181-192 Open Access
    5. Tiffany I. Leung, G. G. van Merode
      Pages 193-212 Open Access
  5. Back Matter
    Pages 213-219

About this book

Introduction

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 (big data), data stewardship (FAIR data) and  related privacy concerns. Aspects of  predictive modelling  using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare.

 

Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.


Keywords

eHealth mHealth Predictive analytics Machine learning Personalized medicine Value based healthcare Big data Clinical Decision Support Systems Open Access

Editors and affiliations

  • Pieter Kubben
    • 1
  • Michel Dumontier
    • 2
  • Andre Dekker
    • 3
  1. 1.Department of NeurosurgeryMaastricht UniversityMaastrichtThe Netherlands
  2. 2.Institute of Data ScienceMaastricht UniversityMaastrichtThe Netherlands
  3. 3.Maastro ClinicMaastrichtThe Netherlands

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-99713-1
  • Copyright Information The Editor(s) (if applicable) and The Author(s) 2019
  • License CC BY
  • Publisher Name Springer, Cham
  • eBook Packages Medicine
  • Print ISBN 978-3-319-99712-4
  • Online ISBN 978-3-319-99713-1
  • Buy this book on publisher's site
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