Abstract
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.
Highlights
The disease characterized by making the patient constantly exhibit elevated levels of blood glucose is referred to as diabetes mellitus
Devices such as Blood Glucose Meters (BGMs) and CGMs do not allow the recording of additional information, such as insulin dosage and food intake, they are useful for providing information on the current level of glucose
The data outputted by Continuous Glucose Monitoring Systems (CGMS) and BGMs is on a continuous domain, which leads the data to be discredited for it to work on traditional classification methods
Summary
The disease characterized by making the patient constantly exhibit elevated levels of blood glucose is referred to as diabetes mellitus. Devices such as BGMs and CGMs do not allow the recording of additional information, such as insulin dosage and food intake, they are useful for providing information on the current level of glucose. An opportunity opens up for an integration between a system that records information on physical activity, food intake, and insulin doses, with another system that maintains glucose measurement records, with increased analytical capacity presenting more elaborate views. This system could use this combinatorial data explosion to find rules, model a patient’s profile, and gain relevant knowledge for patients and physicians. A problem with making data discrete is the loss of information, which might induce errors on the prediction model
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
