Work

Parameters Relevance of a Glucose-Insulin Model in Type 1 Diabetes is Dependent on Meal Behavior

Year: 2025

Type: article

Abstract: Glucose prediction models are essential for managing type 1 diabetes through digital therapeutics. Achieving reliable predictions requires precise calibration of key model parameters, which is non-trivial. This paper introduces a novel methodology for ranking parameters of Hovorka’s glucose-insulin model based on their influence on model output variance. It highlights how the fed-fast cycle alters parameter importance, emphasizing the need for real-time model adaptation. Calibration of selected parameters using this methodology results in a 90% and 32% reduction in Root Mean Square Error (RMSE) in simulated and real data, respectively, demonstrating the approach’s efficacy.

Source: IFAC-PapersOnLine

DOI: 10.1016/j.ifacol.2025.06.021

Authors: Clara Escorihuela-Altaba, Vihangkumar V. Naik, Eleonora Manzoni, Jose Garcia-Tirado

Institutions: University of Bern, University Hospital of Bern, Diabetes Center Berne

Language: English

Topic: Diabetes Management and Research

Subfield: Endocrinology, Diabetes and Metabolism

Field: Medicine

Domain: Health Sciences

Open Access status: diamond

OpenAlex ID: W4412437352

FWCI:

Cites: 15

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Related to: 10