Machine Learning-Based Time in Patterns for Blood Glucose Fluctuation Pattern Recognition in Type 1 Diabetes Management: Development and Validation Study.

Journal: JMIR AI
Published Date:

Abstract

BACKGROUND: Continuous glucose monitoring (CGM) for diabetes combines noninvasive glucose biosensors, continuous monitoring, cloud computing, and analytics to connect and simulate a hospital setting in a person's home. CGM systems inspired analytics methods to measure glycemic variability (GV), but existing GV analytics methods disregard glucose trends and patterns; hence, they fail to capture entire temporal patterns and do not provide granular insights about glucose fluctuations.

Authors

  • Nicholas Berin Chan
    Informatics Research Centre, Henley Business School, University of Reading, Reading, United Kingdom.
  • Weizi Li
    Informatics Research Centre, Henley Business School, University of Reading, Reading, United Kingdom.
  • Theingi Aung
    Royal Berkshire NHS Foundation Trust, Reading, United Kingdom.
  • Eghosa Bazuaye
    Royal Berkshire NHS Foundation Trust, Reading, United Kingdom.
  • Rosa M Montero
    King's College London, London, United Kingdom.

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