A new definition of qualified gain in a data fusion process: application to telemedicine

Abstract

A formal framework is proposed for defining data fusion processes. Particularly the notion of qualified gain is proposed: gain related to representation, completeness, accuracy and certainty. These notions are applied to a medical monitoring and diagnosis problem where a dynamic Bayesian network is used to model time series of observations and evolving states. The model aims at giving a daily diagnosis. Experiments are under way using data of an already existing system collected on kidney disease patients. Results are be characterized using our notion of qualified gains.