Modelling human glucose metabolism in Diabetes Mellitus patients requires the development of complex algorithms and the measurement of a number of parameters. Different approaches to glucose metabolism modelling have been investigated in the literature.

We aim to develop dynamic models of the metabolic behaviour of insulin-treated diabetic patients (either type-1 or type 2) to predict the influence of specific parameters on glucose level and provide decision support to both the patient and the treating physician. The model of the glucose metabolism of diabetic patients will provide predictions of patient’s blood glucose value based on both traditional data (e.g. insulin and exogenous glucose characteristics, subcutaneous glucose concentration) and contextual patient-specific data such as dietary habits, physical activity and energy expenditure. All the available data will form a database that will be used to generate patient-specific models of the glycaemic profile. Methods based on pattern and computational intelligence, time series analysis, fuzzy modelling and data mining will be used in order to analyse the main processes affecting glucose metabolism in diabetic patients and implement a glycaemic profile regarding an individual.