Integrating model-based decision support in a multi-modal reasoning system for managing type 1 diabetic patients.
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.
Montani
Stefania
S
DISTA, Università del Piemonte Orientale A. Avogadro, Alessandria, Italy. stefania@mfn.unipmn.it
Magni
Paolo
P
Bellazzi
Riccardo
R
Larizza
Cristiana
C
Roudsari
Abdul V
AV
Carson
Ewart R
ER
D001185:Artificial Intelligence; D020000:Decision Support Systems, Clinical; D003922:Diabetes Mellitus, Type 1 / Q000628:therapy*; D019468:Disease Management; D006801:Humans; D011338:Probability Theory