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Fundamentals of Clinical Data Science

Editors: Kubben, Pieter, Dumontier, Michel, Dekker, Andre (Eds.)

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  • Provides a resource for healthcare professionals on smart algorithms 
  • Integrates the data, modelling, clinical application levels of clinical data science
  • Focuses on relevant non math and code aspects for physicians
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eBook  
  • ISBN 978-3-319-99713-1
  • This book is an open access book, you can download it for free on link.springer.com
Hardcover $59.99
price for USA in USD
  • ISBN 978-3-319-99712-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

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.


About the authors

Pieter Kubben is a neurosurgeon, mobile app developer and programme manager for eHealth and mHealth for the Maastricht University Medical Center. Telemonitoring and corresponding algorithm development is a particular focus area Dr Kubben is involved in, as well as interactive clinical decision support systems.

Michel Dumontier is a distuinguished professor of data science at Maastricht University and head of the Institute for Data Science – connecting data science initiatives and projects from all faculties. He is also deeply involved in the FAIR data approach (Findable, Accessible, Interoperable, Reproducible).

André Dekker is a professor of clinical data science at Maastricht University and has been leading the development of prediction models in radiation therapy for many years. He is also coordinator of the Personal Health Train project, aiming to facilitate “citizen science”.


Table of contents (15 chapters)

Table of contents (15 chapters)
  • Data Sources

    Pages 3-9

    Kubben, Pieter

  • Data at Scale

    Pages 11-17

    Traverso, Alberto, PhD (et al.)

  • Standards in Healthcare Data

    Pages 19-36

    Schulz, Stefan (et al.)

  • Research Data Stewardship for Healthcare Professionals

    Pages 37-53

    Jansen, Paula (et al.)

  • The EU’s General Data Protection Regulation (GDPR) in a Research Context

    Pages 55-71

    Mondschein, Christopher F. (et al.)

Buy this book

eBook  
  • ISBN 978-3-319-99713-1
  • This book is an open access book, you can download it for free on link.springer.com
Hardcover $59.99
price for USA in USD
  • ISBN 978-3-319-99712-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Fundamentals of Clinical Data Science
Editors
  • Pieter Kubben
  • Michel Dumontier
  • Andre Dekker
Copyright
2019
Publisher
Springer International Publishing
Copyright Holder
The Editor(s) (if applicable) and The Author(s)
eBook ISBN
978-3-319-99713-1
DOI
10.1007/978-3-319-99713-1
Hardcover ISBN
978-3-319-99712-4
Edition Number
1
Number of Pages
VIII, 219
Number of Illustrations
10 b/w illustrations, 35 illustrations in colour
Topics