Personalized Meal Bolus Calculator for Type-1 Diabetes Accounting for Diurnal Effects

Type 1 diabetes management requires compensating carbohydrate intake with bolus insulin matched to the meal size. Recent clinical studies revealed diurnal variations in insulin sensitivity (SI) in patients with type 1 diabetes, where the insulin resistance varies over the day. Diurnal variations in insulin sensitivity requires different bolus insulin dose for the same meal size depending on the time of the meal. Standard bolus calculators that use patient-specific parameters such as insulin-carb-ratio (CR) and correction factors (CF), however do not account for such diurnal variations. To address this gap, this paper proposes a fully data-driven safe and personalized bolus calculator that explicitly accounts for the diurnal variations. The proposed algorithm safely learns the optimum bolus needs tailored to each patient without the need for any patient-specific parameters such as carb-ratio, correction factor, insulin sensitivity etc., nor any historical clinical data. The proposed algorithm is tested and verified on the 10-adult cohort of the FDA-accepted UVA/Padova T1DM simulator.

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Publication Details

Journal
IFAC-PapersOnLine
Published
2025-01-01
DOI
https://doi.org/10.1016/j.ifacol.2025.06.023
Primary Topic
Diabetes Management and Research
Type
article
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article

Personalized Meal Bolus Calculator for Type-1 Diabetes Accounting for Diurnal Effects

Dinesh Krishnamoorthy, Francis J. Doyle
IFAC-PapersOnLine
Diabetes Management and Research
article

Personalized Meal Bolus Calculator for Type-1 Diabetes Accounting for Diurnal Effects

Dinesh Krishnamoorthy, Francis J. Doyle
article en

Abstract

Type 1 diabetes management requires compensating carbohydrate intake with bolus insulin matched to the meal size. Recent clinical studies revealed diurnal variations in insulin sensitivity (SI) in patients with type 1 diabetes, where the insulin resistance varies over the day. Diurnal variations in insulin sensitivity requires different bolus insulin dose for the same meal size depending on the time of the meal. Standard bolus calculators that use patient-specific parameters such as insulin-carb-ratio (CR) and correction factors (CF), however do not account for such diurnal variations. To address this gap, this paper proposes a fully data-driven safe and personalized bolus calculator that explicitly accounts for the diurnal variations. The proposed algorithm safely learns the optimum bolus needs tailored to each patient without the need for any patient-specific parameters such as carb-ratio, correction factor, insulin sensitivity etc., nor any historical clinical data. The proposed algorithm is tested and verified on the 10-adult cohort of the FDA-accepted UVA/Padova T1DM simulator.

IFAC-PapersOnLineVol. 59(2)
Brown University (US), Eindhoven University of Technology (NL)
Zero hunger
Openalex Percentile: Top 75%
Diabetes Management and Research
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