Discriminative Distance Functions and the Patient Neighborhood Graph for Clinical Decision Support

  • Alexey Tsymbal
  • Martin Huber
  • Shaohua Kevin Zhou
Conference paper
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 680)


There are two essential reasons for the slow progress in the acceptance of clinical similarity search-based decision support systems (DSSs); the especial complexity of biomedical data making it difficult to define a meaningful and effective distance function and the lack of transparency and explanation ability in many existing DSSs. In this chapter, we address these two problems by introducing a novel technique for visualizing patient similarity with neighborhood graphs and by considering two techniques for learning discriminative distance functions. We present an experimental study and discuss our implementation of similarity visualization within a clinical DSS.


Clinical Decision Support System Similarity Search Distance Learning Neighborhood Graph Case Retrieval 



This work has been partially funded by the EU project Health-e-Child (IST 2004-027749).


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Alexey Tsymbal
    • 1
  • Martin Huber
  • Shaohua Kevin Zhou
  1. 1.Corporate Technology Div. Siemens AGErlangenGermany

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