Name
Title | ||
|---|---|---|
Bayesian Model Selection Framework to Improve Calibration of Continuous Glucose Monitoring Sensors for Diabetes Management |
Abstract | ||
|---|---|---|
Minimally-invasive continuous glucose monitoring (CGM) sensors have revolutionized perspectives in the treatment of type 1 diabetes (T1D). Their accuracy relies on an internal calibration function that transforms the raw, physically measured, electrical data into blood glucose concentration values. Usually, a unique, pre-determined, calibration functional is adopted, with parameters periodically updated in individual patients by using “gold standard” references suitably collected by finger prick devices. However, retrospective analysis of CGM data suggests that variability of sensor-subject characteristics is often inefficiently coped with. In the present study, we propose a conceptual Bayesian model- selection framework aimed at guaranteeing wide margins of flexibility for both the determination of the most appropriate calibration functional and the numerical values of its unknown parameters. The calibration model is determined among a finite specified set of candidates, each one depending on a set of unknown model parameters, for which a priori statistical expectations are available. Model selection is based on predictive distributions carrying out asymptotic calculations through Monte Carlo integration methods. Performance of the proposed approach is assessed on synthetic data generated by a well-established T1D simulation model. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/EMBC.2018.8512240 | 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
Keywords | Field | DocType |
Algorithms,Bayes Theorem,Biometry,Blood Glucose,Blood Glucose Self-Monitoring,Calibration,Diabetes Mellitus, Type 1,Electric Power Supplies,Electricity,Female,Humans,Insulin Infusion Systems,Microsurgery,Models, Theoretical,Monte Carlo Method,Physical Examination,Retrospective Studies | Computer vision,Data mining,Data modeling,Bayesian inference,Computer science,A priori and a posteriori,Diabetes management,Model selection,Synthetic data,Monte Carlo integration,Artificial intelligence,Calibration | Conference |
Volume | ISSN | ISBN |
2018 | 1557-170X | 978-1-5386-3647-3 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giada Acciaroli | 1 | 2 | 1.82 |
| Martina Vettoretti | 2 | 7 | 3.39 |
| Andrea Facchinetti | 3 | 152 | 28.83 |
| Giovanni Sparacino | 4 | 276 | 52.52 |