Forecasting Glucose Concentration in Diabetic Patients Using Ignorant Belief Networks

Artificial Intelligence in Medicine: Interpreting Clinical Data

Volume

Papers from the 1994 AAAI Spring Symposium

Contents

Bayesian Belief Networks (BBNs) provide suitable formalism for many medical applications. Unfortunately, learning a BBN from a database is a very complex task, since the specification of a BBN requires a large amount of information that is not always available in real-world databases. In this paper, we introduce a new class of BBNS, called Ignorant Belief Networks, able to reason with incomplete information. We will show how this new formalism can be used to forecast blood glucose concentration in insulin-dependent diabetic patients using underspecified probabilistic models directly derived from a real-world database.