Managing Diabetes: Counselling Supported by User Data in a Mobile Platform

Abstract

Diabetes management is a complex problem. The patient needs to monitor several parameters in order to react in the most appropriate way. Different situations require the diabetic to understand and evaluate different rules. The main source of knowledge for those rules arises from medical practice and is usually transmitted through medical appointments. Given this initial advice, most patient are on a continuous process of managing the disease, toward achieving the best possible quality of life. Motivated by recent aadvances in diabetes monitoring devices, we introduce a diabetes support system designed to accompany the user, advising her and providing early guidance to avoid some of the many complications associated with diabetes. To accomplish this goal, we incorporate standard medical protocols, advice and directives in a Rule Based System (RBS). This RBS which we call Advice Rule Based System (ARBS) is capable of advising and uncovering possible causes for different occurrences. We believe that this solution is not only beneficial to the patient, but may also may be of use to the clinitians advising the patient. The device has continuous contact with the patient, thus it can provide early response if/where needed, Moreover, the system can provide useful data, that an authorized medical expert can use while prescribing a particular treatment, or even when investingating this health problem. We have started to add data-mining algorithms and methods, to uncover hidden behavioural patterns that may lead to crisis situations. Ultimately, through refining the rule systems base don human and machine learning, our approach has the potential for personalising the system according to the habits and phenotype of its user. The system is to be incorporated in a currently developed diabetes management application for Android.