Integrating model-based decision support in a multi-modal reasoning system for managing type 1 diabetic patients

  • Authors:

  • Stefania Montani;Paolo Magni;Riccardo Bellazzi;Cristiana Larizza;Abdul V. Roudsari;Ewart R. Carson

  • Affiliations:

  • DISTA, Universití del Piemonte Orientale "A. Avogadro", Alessandria, Italy;Dipartimento di Informatica e Sistemistica Universití di Pavia, Pavia, Italy;Dipartimento di Informatica e Sistemistica Universití di Pavia, Pavia, Italy;Dipartimento di Informatica e Sistemistica Universití di Pavia, Pavia, Italy;MIM Center, City University, London, UK;MIM Center, City University, London, UK

  • Venue:
  • Artificial Intelligence in Medicine
  • Year:
  • 2003

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Abstract

We present a multi-modal reasoning (MMR) methodology that integrates case-based reasoning (CBR), rule-based reasoning (RBR) and model-based reasoning (MBR), meant to provide physicians with a reliable decision support tool in the context of type 1 diabetes mellitus management. In particular, we have implemented a decision support system that is able to jointly exploit a probabilistic model of the glucose-insulin system at the steady state, a RBR system for suggestion generation and a CBR system for patient's profiling. The integration of the CBR, RBR and MBR paradigms allows for an optimized exploitation of all the available information, and for the definition of a therapy properly tailored to the patient's needs, overcoming the single approaches limitations. The system has been tested both on simulated and on real patients' data.