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Machine Learning-Based Time in Patterns for Blood Glucose Fluctuation Pattern Recognition in Type 1 Diabetes

Nicholas Berin Chan1, Weizi Li1, Theingi Aung2

  • 1Informatics Research Centre, Henley Business School, University of Reading, Reading, United Kingdom.

JMIR AI
|June 14, 2024
PubMed
Summary

This study introduces a new machine learning method to analyze continuous glucose monitoring (CGM) data, identifying distinct blood glucose fluctuation patterns for better diabetes management. This approach offers clinicians more detailed insights into glycemic variability (GV) and patient groups.

Keywords:
continuous glucose monitoringdiabetes mellitusglucose fluctuation patternglycemic variabilityscalable metricstemporal clustering

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Area of Science:

  • Biomedical Engineering
  • Data Science in Healthcare
  • Diabetes Technology

Background:

  • Continuous Glucose Monitoring (CGM) systems generate vast amounts of data for diabetes management.
  • Existing glycemic variability (GV) analytics often overlook glucose trends and patterns, limiting insights.
  • A need exists for more comprehensive GV analysis that captures temporal patterns and patient-specific fluctuations.

Purpose of the Study:

  • To develop a machine learning framework for recognizing blood glucose fluctuation patterns from CGM data.
  • To create a more informative representation of GV profiles for clinicians.
  • To enable patient stratification based on identified blood glucose fluctuation patterns.

Main Methods:

  • Utilized dynamic time warping to extract prevalent blood glucose fluctuation patterns from 1.5 million CGM measurements in 126 type 1 diabetes mellitus (T1DM) patients.
  • Validated extracted patterns in an independent cohort of 225 T1DM patients in the United States.
  • Applied hierarchical clustering to 'time in patterns' to define 4 distinct patient groups for comparative statistical analysis.

Main Results:

  • Identified and validated 6 distinctive blood glucose fluctuation patterns, leading to the classification of 4 unique GV profiles in T1DM patients.
  • These 4 GV profiles demonstrated significant differences in key glycemic indicators, including diabetes duration, glycated hemoglobin (HbA1c), and time in range (TIR).
  • The identified patient groups exhibited varying management needs based on their distinct glycemic profiles.

Conclusions:

  • The proposed machine learning framework effectively extracts blood glucose fluctuation patterns from CGM data.
  • 'Time in patterns' provides a rich and scalable view of a patient's GV profile, complementing traditional metrics like 'time in range'.
  • This method offers a more accessible and informative approach for clinicians to understand and manage diabetes.