Interactive Knowledge Discovery and Data Mining in Biomedical Informatics

State-of-the-Art and Future Challenges

  • Andreas Holzinger
  • Igor Jurisica

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8401)

Table of contents

  1. Front Matter
  2. David Otasek, Chiara Pastrello, Andreas Holzinger, Igor Jurisica
    Pages 19-33
  3. Katharina Holzinger, Vasile Palade, Raul Rabadan, Andreas Holzinger
    Pages 35-56
  4. Andreas Holzinger, Bernd Malle, Marcus Bloice, Marco Wiltgen, Massimo Ferri, Ignazio Stanganelli et al.
    Pages 57-80
  5. Gilad Katz, Asaf Shabtai, Lior Rokach
    Pages 81-100
  6. Cagatay Turkay, Fleur Jeanquartier, Andreas Holzinger, Helwig Hauser
    Pages 117-140
  7. Roberto Boselli, Mirko Cesarini, Fabio Mercorio, Mario Mezzanzanica
    Pages 141-168
  8. Matthijs van Leeuwen
    Pages 169-182
  9. David Windridge, Miroslaw Bober
    Pages 197-208
  10. Andreas Holzinger, Matthias Hörtenhuber, Christopher Mayer, Martin Bachler, Siegfried Wassertheurer, Armando J. Pinho et al.
    Pages 209-226
  11. Hoan Nguyen, Julie D. Thompson, Patrick Schutz, Olivier Poch
    Pages 255-270
  12. Andreas Holzinger, Johannes Schantl, Miriam Schroettner, Christin Seifert, Karin Verspoor
    Pages 271-300
  13. Peter Kieseberg, Heidelinde Hobel, Sebastian Schrittwieser, Edgar Weippl, Andreas Holzinger
    Pages 301-316
  14. Andreas Holzinger
    Pages 331-356
  15. Back Matter

About this book


One of the grand challenges in our digital world are the large, complex, and often weakly structured data sets and massive amounts of unstructured information. This “big data” challenge is most evident in biomedical informatics: The trend toward precision medicine has resulted in an explosion in the amount of biomedical data sets generated. Despite the fact that human experts are very good at pattern recognition in three dimensions or less, most of the data are high-dimensional, which makes manual analysis often impossible and neither the medical doctor nor the biomedical researcher can memorize all these facts. A synergistic combination of the methodologies and approaches of two fields offer ideal conditions for unraveling these problems: human–computer interaction (HCI) and knowledge discovery/data mining (KDD), with the goal of supporting human capabilities with machine learning.

This state-of-the-art survey is an output of the HCI-KDD expert network and features 19 carefully selected and reviewed papers related to seven hot and promising research areas: (1) data integration, data pre-processing, and data mapping; (2) data mining algorithms; (3) graph-based data mining; (4) entropy-based data mining; (5) topological data mining; (6)  visualization; (7) privacy, data protection, safety, and security.




big data biomedical informatics computing methodologies data analytics data mining feature selection graph theory health informatics healthcare data human computer interaction knowledge discovery machine learning privacy text mining visualization

Editors and affiliations

  • Andreas Holzinger
    • 1
  • Igor Jurisica
    • 2
  1. 1.Research Unit Human-Computer Interaction, Austrian IBM Watson Think Gruop, Institute for Medical Informatics, Statistics and DocumentationMedical University of GrazGrazAustria
  2. 2.IBM Life Sciences Discovery Centre, TECHNA for the Advancement of Technology for HealthPrincess Margaret Cancer Centre, University Health NetworkTorontoCanada

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2014
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science Computer Science (R0)
  • Print ISBN 978-3-662-43967-8
  • Online ISBN 978-3-662-43968-5
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site
Industry Sectors
Finance, Business & Banking
IT & Software
Consumer Packaged Goods
Energy, Utilities & Environment
Oil, Gas & Geosciences