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

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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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Authors and Affiliations

  1. Rudjer Bošković Institute, Bijenička 54, 10000, Zagreb, Croatia

    Dragan Gamberger

  2. Jožef Stefan Institute, Jamova 39, 1000, Ljubljana, Slovenia

    Nada Lavrač

  3. University of Nova Gorica, Vipavska 13, 5000, Nova Gorica, Slovenia

    Nada Lavrač

  4. Department of Neurology, University Hospital of Traumatology, Draškovićeva 19, 10000, Zagreb, Croatia

    Antonija Krstačić

  5. Institute for Cardiovascular Diseases and Rehabilitation, Draškovićeva 13, 10000, Zagreb, Croatia

    Goran Krstačić

Authors
  1. Dragan Gamberger
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  2. Nada Lavrač
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  3. Antonija Krstačić
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  4. Goran Krstačić
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Corresponding author

Correspondence to Dragan Gamberger.

Additional information

This work was supported by Croatian Ministry of Science, Education and Sport project “Machine Learning Algorithms and Applications”, Slovenian Ministry of Higher Education, Science and Technology project “Knowledge Technologies”, and EU FP6 project “Heartfaid: A knowledge based platform of services for supporting medical–clinical management of the heart failure within the elderly population”.

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Gamberger, D., Lavrač, N., Krstačić, A. et al. Clinical data analysis based on iterative subgroup discovery: experiments in brain ischaemia data analysis. Appl Intell 27, 205–217 (2007). https://doi.org/10.1007/s10489-007-0068-9

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