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.

中文翻译：

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迈向可解释的连续血糖监测数据建模


糖尿病带来的持续的全球健康挑战需要对传感器生成的所有数据流进行批判性的了解。为了解决这个问题，我们的研究提出了一种基于模糊逻辑的葡萄糖传感器数据描述性分析。该分析嵌入在旨在支持多患者监测的创新架构的背景下，旨在协助医疗保健专业人员完成日常任务并提供必要的决策工具。我们的新颖方法可以捕获和解释来自葡萄糖传感器的复杂数据模式，并且还引入了创建高质量语言摘要的功能，以通过使用自然语言 (NL) 突出显示最相关的现象。这些描述有助于医疗保健专业人员和糖尿病患者之间的清晰沟通，增强对复杂数据模式的更深入理解并促进糖尿病护理方面的合作。我们的提案与使用 GPT-4 获得的提案之间的比较评估强调了我们方法的可持续性、有效性和效率，将其定位为在护理和预防方面赋予糖尿病患者权力的新标准，为他们的进步和福祉做出贡献。