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Automatic Adaptation of Basal Insulin Using Sensor-Augmented Pump Therapy.

Pau Herrero¹, Jorge Bondia², Marga Giménez³

¹1 Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London, UK.

Journal of Diabetes Science and Technology

|March 2, 2018

Summary

This summary is machine-generated.

A new run-to-run control law effectively adapts basal insulin profiles for people with type 1 diabetes on insulin pumps. This AI-driven technique significantly improves time in target glucose range and reduces hypoglycemia, enhancing glycemic control.

Keywords:

adaptive control artificial intelligence basal insulin case-based reasoning run-to-run type 1 diabetes

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Area of Science:

Biomedical Engineering
Artificial Intelligence in Medicine
Endocrinology

Background:

Intensified insulin regimens are crucial for insulin-dependent diabetes but often impractical, leading to suboptimal glycemic control.
Existing guidelines for basal insulin adjustment in sensor-augmented pump therapy have limitations.
A novel technique for automatic basal insulin profile adaptation is presented.

Purpose of the Study:

To introduce and validate a novel run-to-run control law for automatic basal insulin profile adaptation.
To address limitations of previous methods in managing basal insulin requirements.
To explore the use of case-based reasoning for handling variability in insulin needs.

Main Methods:

Development of a novel run-to-run control law for basal insulin adaptation.
In silico validation of the proposed control law.
Proposal of case-based reasoning (an artificial intelligence technique) to manage basal insulin variability.

Main Results:

The run-to-run control law successfully adapted basal insulin profiles in a virtual population (adults, adolescents, children) over 4 months.
Significant improvements observed in percentage of time in target glucose range ([70, 180] mg/dl): Adults (70.9% to 91.1%), Adolescents (46.5% to 80.1%), Children (49.4% to 73.7%).
Significant reductions in percentage of time in hypoglycemia (<70 mg/dl): Adults (9.7% to 0.9%), Adolescents (10.5% to 0.83%), Children (10.9% to 3.2%). The new method outperformed existing techniques in reducing hypoglycemia without increasing hyperglycemia.

Conclusions:

The novel technique demonstrates significant potential for effectively adjusting basal insulin profiles in type 1 diabetes patients using sensor-augmented insulin pump therapy.
The AI-driven approach offers a promising solution for improving glycemic outcomes in this population.
Further research may explore real-world clinical applications and refinements of the case-based reasoning approach.