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Temporal Abstractions and Case-Based Reasoning for Medical Course Data: Two Prognostic Applications

Rainer Schmidt2 and Lothar Gierl2

(2)  Institut für Medizinische Informatik und Biometrie, Universität Rostock, D-18055 Rostock, Germany
Abstract
We have developed a method for analysis and prognosis of multiparametric kidney function courses. The method combines two abstraction steps (state abstraction and temporal abstraction) with Case-based Reasoning. Recently we have started to apply the same method in the domain of Geomedicine, namely for the prognosis of the temporal spread of diseases, mainly of influenza, where just one of the two abstraction steps is necessary, that is the temporal one. In this paper, we present the application of our method in the kidney function domain, show how we are going to apply the same ideas for the prognosis of the spread of diseases, and summarise the main principles of the method.

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Referenced by
4 newer articles

  1. Perner, Petra (2008) Case-based reasoning and the statistical challenges. Quality and Reliability Engineering International
    [CrossRef]
  2. Montani, Stefania (2008) Exploring new roles for case-based reasoning in heterogeneous AI systems for medical decision support. Applied Intelligence 28(3)
    [CrossRef]
  3. Perner, Petra (2008) Prototype-based classification. Applied Intelligence 28(3)
    [CrossRef]
  4. Funk, Peter (2006) CASE-BASED REASONING AND KNOWLEDGE DISCOVERY IN MEDICAL APPLICATIONS WITH TIME SERIES. Computational Intelligence 22(3-4)
    [CrossRef]
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