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Combining statistical learning with a knowledge-based approach: A case study in intensive care monitoring

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  • Morik, Katharina
  • Brockhausen, Peter
  • Joachims, Thorsten

Abstract

The paper describes a case study in combining different methods for acquiring medical knowledge. Given a huge amount of noisy, high dimensional numerical time series data describing patients in intensive care, the support vector machine is used to learn when and how to change the dose of which drug. Given medical knowledge about and expertise in clinical decision making, a first-order logic knowledge base about effects of therapeutical interventions has been built. As a preprocessing mechanism it uses another statistical method. The integration of numerical and knowledge-based procedures eases the task of validation in two ways. On one hand, the knowledge base is validated with respect to past patients records. On the other hand, medical interventions that are recommended by learning results are justified by the knowledge base.

Suggested Citation

  • Morik, Katharina & Brockhausen, Peter & Joachims, Thorsten, 1999. "Combining statistical learning with a knowledge-based approach: A case study in intensive care monitoring," Technical Reports 1999,24, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    • Handle: RePEc:zbw:sfb475:199924
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    Cited by:

    1. Bo Huang & Chenglin Xie & Richard Tay & Bo Wu, 2009. "Land-Use-Change Modeling Using Unbalanced Support-Vector Machines," Environment and Planning B, , vol. 36(3), pages 398-416, June.
    2. Lisa M Breckels & Sean B Holden & David Wojnar & Claire M Mulvey & Andy Christoforou & Arnoud Groen & Matthew W B Trotter & Oliver Kohlbacher & Kathryn S Lilley & Laurent Gatto, 2016. "Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics," PLOS Computational Biology, Public Library of Science, vol. 12(5), pages 1-26, May.

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