A Diabetes Management Information System with Glucose Prediction

Diabetes has become a serious health concern. The use and popularization of blood glucose measurement devices have led to a tremendous increase on health for diabetics. Tracking and maintaining traceability between glucose measurements, insulin doses and carbohydrate intake can provide useful information to physicians, health professionals, and patients. This paper presents an information system, called GLUMIS (GLUcose Management Information System), aimed to support diabetes management activities. It is made of two modules, one for glucose prediction and one for data visualization and a reasoner to aid users in their treatment. Through integration with glucose measurement devices, it is possible to collect historical data on the treatment. In addition, the integration with a tool called the REALI System allows GLUMIS to also process data on insulin doses and eating habits. Quantitative and qualitative data were collected through an experimental case study involving 10 participants. It was able to demonstrate that the GLUMIS system is feasible. It was able to discover rules for predicting future values of blood glucose by processing the past history of measurements. Then, it presented reports that can help diabetics choose the amount of insulin they should take and the amount of carbohydrate they should consume during the day. Rules found by using one patient’s measurements were analyzed by a specialist that found three of them to be useful for improving the patient’s treatment. One such rule was “if glucose before breakfast ∈ [ 47 , 89 ] , then glucose at afternoon break in [ 160 , 306 ]„. The results obtained through the experimental study and other verifications associated with the algorithm created had a double objective. It was possible to show that participants, through a questionnaire, viewed the visualizations as easy, or very easy, to understand. The secondary objective showed that the innovative algorithm applied in the GLUMIS system allows the decision maker to have much more precision and less loss of information than in algorithms that require the data to be discretized