A PROBABILISTIC APPROACH TO GLUCOSE PREDICTION AND INSULIN DOSE ADJUSTMENT - DESCRIPTION OF METABOLIC MODEL AND PILOT EVALUATION STUDY

A model of carbohydrate metabolism has been implemented as a causal pr obabilistic network, allowing explicit representation of the uncertain ties involved in the prediction of 24-h blood glucose profiles in insu lin-dependent diabetic subjects. The parameters of the model were base d on experimental data from the literature describing insulin and carb ohydrate absorption, renal loss of glucose, insulin-independent glucos e utilisation and insulin-dependent glucose utilisation and production . The model can be adapted to the observed glucose metabolism in the i ndividual patient and can be used to generate predicted 24-h blood glu cose profiles. A penalty is assigned to each level of blood glucose, t o indicate that high and low blood glucose levels are undesirable. The system can be asked to find the insulin doses that result in the most desirable 24-h blood glucose profile. In a series of 12 patients, the system predicted blood glucose with a mean error of 3.3 mmol/l. The i nsulin doses suggested by the system seemed reasonable and in several cases seemed more appropriate than the doses actually administered to the patients.