Clustering type 1 diabetes patients based on blood glucose level measurements

Abstract

Improving diabetes mellitus management requires accurate blood glucose predictions to prevent dangerous glycemic extreme events; however, traditional models often struggle with the inherent fluctuations in blood glucose levels. In this work, the identification of distinct patient groups exhibiting common characteristics based on longitudinal continuous glucose monitoring (CGM) data is explored. This clustering approach aims to offer valuable insights for both enhancing predictive modeling accuracy and informing clinical relevance. Raw CGM data was transformed into structured feature sets using statistical descriptors calculated across clinically meaningful time intervals. Following outlier removal, multiple clustering algorithms were evaluated, with the optimal solution selected based on internal metrics and validation by clinical experts. The analysis revealed six distinct patient groups, each characterized by unique glycemic behaviors, including well-controlled, prone to hyperglycemia, and prone to hypoglycemia profiles. It is proposed that these identified clusters can improve glucose prediction by strategically balancing personalized and generalized approaches. Furthermore, valuable insights into individual metabolic variability can be obtained, potentially supporting the development of tailored treatment strategies. While acknowledging limitations inherent in data transformation and expert-driven evaluation, this clustering methodology represents a significant step towards more precise and data-driven diabetes mellitus management.