Applied Intelligence

, Volume 27, Issue 3, pp 205–217 | Cite as

Clinical data analysis based on iterative subgroup discovery: experiments in brain ischaemia data analysis

  • Dragan Gamberger
  • Nada Lavrač
  • Antonija Krstačić
  • Goran Krstačić
Article

Abstract

This paper presents a case study of the process of insightful analysis of clinical data collected in regular hospital practice. The approach is applied to a database describing patients suffering from brain ischaemia, either permanent as brain stroke with positive computer tomography (CT) or reversible ischaemia with normal brain CT test. The goal of the analysis is the extraction of useful knowledge that can help in diagnosis, prevention and better understanding of the vascular brain disease. This paper demonstrates the applicability of subgroup discovery for insightful data analysis and describes the expert’s process of converting the induced rules into useful medical knowledge. Detection of coexisting risk factors, selection of relevant discriminative points for numerical descriptors, as well as the detection and description of characteristic patient subpopulations are important results of the analysis. Graphical representation is extensively used to illustrate the detected dependencies in the available clinical data.

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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Dragan Gamberger
    • 1
  • Nada Lavrač
    • 2
    • 3
  • Antonija Krstačić
    • 4
  • Goran Krstačić
    • 5
  1. 1.Rudjer Bošković InstituteZagrebCroatia
  2. 2.Jožef Stefan InstituteLjubljanaSlovenia
  3. 3.University of Nova GoricaNova GoricaSlovenia
  4. 4.Department of NeurologyUniversity Hospital of TraumatologyZagrebCroatia
  5. 5.Institute for Cardiovascular Diseases and RehabilitationZagrebCroatia

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