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Identifying and Intervening on Glucose Patterns in Multivariate Data Using Block-Based Recurrence Quantification

Taisa Kushner1,2, Clara Mosquera-Lopez1, Wade Hilts1

  • 1Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Laboratory, Oregon Health & Science University, Portland, OR, USA.

Journal of Diabetes Science and Technology
|November 1, 2025
PubMed
Summary
This summary is machine-generated.

A new Block-based Recurrence Quantification Analysis (BlockRQA) method effectively identifies complex patterns in type 1 diabetes (T1D) data. This technique improves automated insulin delivery (AID) systems by targeting hyperglycemia-inducing behaviors.

Keywords:
artificial intelligenceautomated insulin deliveryhyperglycemiahypoglycemiapattern recognition

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Area of Science:

  • Biomedical Engineering
  • Data Science
  • Endocrinology

Background:

  • Automated insulin delivery (AID) systems enhance glycemic control in type 1 diabetes (T1D).
  • Existing methods struggle to identify complex, multidimensional patterns linked to hypo- and hyperglycemia without bias.
  • There's a need for advanced pattern recognition in mixed-type time-series data for T1D management.

Purpose of the Study:

  • Introduce Block-based Recurrence Quantification Analysis (BlockRQA) for pattern detection in T1D data.
  • Demonstrate the feasibility of integrating BlockRQA with AID systems (BlockRQA+AID).
  • Identify and address patterns leading to hyperglycemia in individuals with T1D.

Main Methods:

  • Developed BlockRQA, extending Recurrence Quantification Analysis for categorical and continuous time-series data.
  • Applied BlockRQA to identify interpretable patterns without data embeddings.
  • Integrated BlockRQA with an existing AID system for real-time pattern analysis and dosing adjustments.

Main Results:

  • BlockRQA+AID demonstrated improved glucose outcomes in silico for hyperglycemia-related patterns.
  • An outpatient pilot study showed BlockRQA+AID reduced hyperglycemic events (>250 mg/dL).
  • BlockRQA efficiently identified, aggregated, and scored behavioral patterns for clinical intervention.

Conclusions:

  • Block-based Recurrence Quantification Analysis (BlockRQA) is a potent tool for pattern recognition in glucose control.
  • BlockRQA can identify glucose outcome patterns to optimize AID dosing strategies.
  • This technique holds promise for personalized management of type 1 diabetes.