Learning from biomedical time series through the integration of qualitative models and fuzzy systems

R. Bellazzi, R. Guglielmann, L. Ironi

Research output: Contribution to journalArticlepeer-review

Abstract

Our work deals with a method for the identification of the dynamics of nonlinear (patho-)physiological systems by learning from data. The key idea which underlies our approach consists in the integration of qualitative modeling methods with fuzzy logic systems. The major advantage which derives from such an integrated framework lies in its capability both to represent the structural knowledge of the system at study and to determine, by exploiting the available experimental data, a functional approximation of the system dynamics that can be used as a reasonable predictor of the patient's future state. We have successfully applied our method in the identification of the intracellular kinetics of thiamine from data collected in the intestine cells.

Original languageEnglish
Pages (from-to)215-220
Number of pages6
JournalArtificial Intelligence in Medicine
Volume21
Issue number1-3
DOIs
Publication statusPublished - Jan 2001

Keywords

  • Fuzzy logic systems
  • Nonlinear system identification
  • Qualitative reasoning
  • Thiamine kinetics

ASJC Scopus subject areas

  • Artificial Intelligence
  • Medicine(all)

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