Toward an Interpretable Continuous Glucose Monitoring Data Modeling

Abstract

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 multipatient 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.