Learning from data through the integration of qualitative models and fuzzy systems
by R. Bellazzi, L. Ironi, R. Guglielmann, M. Stefanelli
in: E. Keravnou, C. Garbay, R. Baud, J. Wyatt (eds.), Lecture Notes in Artificial Intelligence.
ABSTRACT
This paper presents a method for the identification of the dynamics of
non-linear 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 exploit the available experimental data, so that a
functional approximation of the system dynamics can be determined and
used as a reasonable predictor of the patient's future state. As
testing ground of our method, we have considered the problem of
identifying the response to the insulin therapy from insulin-dependent
diabetic patients: the results obtained are presented and discussed in
the paper.
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