Sparse Reconstruction of Glucose Fluxes using Continuous Glucose Monitors

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

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

A new technique for estimating postprandial glucose flux profiles without the use of glucose tracers is proposed. The technique assumes knowledge of patient parameters relevant to the glucose, insulin and endogoneous glucose production subsystems. A convex Lasso formulation is used to estimate the glucose fluxes that combines (1) the known patient parameters; (2) a sparse vector space encoding the 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 estimated: glucose rate of appearance from the intestine; endogenous glucose production from the liver; insulin dependent glucose utilization and other important state variables. Sparse encoding of a large set of simulated glucose fluxes using the UVa Padova simulator is used so that a sparse representation of the space of plausible glucose flux profiles is obtained. The estimation technique was validated in both simulation and experiments on 3 T1DM patients undergoing the triple tracer meal protocol. The results indicate that the technique is capable of reproducing the triple tracer measurements while estimating the missing measurements for a certain model parameter selection.
Original languageEnglish (US)
Pages (from-to)1-1
Number of pages1
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
DOIs
StatePublished - 2019

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