Towards an Interpretable Continuous Glucose Monitoring Data Modeling

The ongoing global health challenge posed by diabetes necessitates a critical understanding of all generated data streamed from sensors. To address this, our study presents a robust fuzzy logic-based descriptive analysis of glucose sensor data. This analysis is embedded within the context of an innovative architecture designed to support multi-patient monitoring, with the goal of assisting healthcare professionals in their daily tasks and providing essential decision-making tools. Our novel approach, captures and interprets complex data patterns from glucose sensors, and also introduces the capability of creating high-quality linguistic summaries, to highlight the most relevant phenomena through the use of natural language (NL). These descriptions facilitate clear communication between healthcare professionals and people with diabetes, enhancing a deeper understanding of intricate data patterns and promoting collaboration in diabetes care. A comparative evaluation between our proposal and the one obtained using GPT-4 underscores the sustainability, effectiveness and efficiency of our methodology, positioning it as a new standard for empowering diabetic patients in terms of care and prevention, contributing to their progress and well-being. Authors