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

  • Authors:
  • Rainer Schmidt;Lothar Gierl

  • Affiliations:
  • -;-

  • Venue:
  • MLDM '01 Proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition
  • Year:
  • 2001

Quantified Score

Hi-index 0.00

Visualization

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.