Personalized and context-aware decision support system for diabetes therapeutics

Abstract

Personalized recommendations for diabetes therapy can be highly beneficial but complex to make and time-consuming for patients and healthcare professionals. Therefore, we propose a decision support system to help patients and healthcare professionals to make decisions to improve treatments based on the patient’s daily life data. The proposed solution allows the collection of patients’ data related to their glycemic profile, insulin doses, carbohydrate intake, physical activity, and patient weight. This data provides information for a case-based reasoning system to predict postprandial blood glucose allowing the calculation improvement of bolus insulin. This decision support system aims to reduce patients’ hypo and hyperglycemic episodes and improve the patients’ time in range.