Sparse Reconstruction of Glucose Fluxes Using Continuous Glucose Monitors

Ali A. Al-Matouq, Taous Meriem Laleg-Kirati, Carlo Novara, Ivana Rabbone, Tyrone Vincent

Risultato della ricerca: Contributo su rivistaArticolo in rivistapeer review

Abstract

A new technique for estimating postprandial glucose flux profiles without the use of glucose tracers is proposed. A sparse vector space representation is first found for the space of plausible glucose flux profiles using sparse encoding. A Lasso formulation is then used to estimate the glucose fluxes that combines (1) known patient model parameters; (2) the vector space of plausible glucose flux profiles; (3) continuous glucose monitor measurements taken during the meal; (4) amount of insulin injected; (5) amount of meal carbohydrates; and (6) an estimate of the initial conditions. Three glucose fluxes are then estimated, namely; glucose rate of appearance from the intestine; endogenous glucose production from the liver; insulin dependent glucose utilization; and other important state variables. The simulation results show that the technique is capable of estimating the glucose fluxes with high accuracy, even for complex meal scenarios. The experimental results indicate that the technique is capable of reproducing the triple tracer measurements for three T1DM undergoing the triple tracer protocol while estimating the missing measurements for a certain model parameter selection.

Lingua originaleInglese
pagine (da-a)1797-1809
Numero di pagine13
RivistaIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume17
Numero di pubblicazione5
DOI
Stato di pubblicazionePubblicato - set 2020
Pubblicato esternamente

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