Name
Title | ||
|---|---|---|
A therapy parameter-based model for predicting blood glucose concentrations in patients with type 1 diabetes |
Abstract | ||
|---|---|---|
HighlightsNew glucose prediction model for the treatment of type 1 diabetes.Strongly correlation of model parameters with therapy parameters.Equivalent predictions to models with 50% more parameters.Validation on clinical data as well as UVa simulation data. In this paper, the problem of predicting blood glucose concentrations (BG) for the treatment of patients with type 1 diabetes, is addressed. Predicting BG is of very high importance as most treatments, which consist in exogenous insulin injections, rely on the availability of BG predictions. Many models that can be used for predicting BG are available in the literature. However, it is widely admitted that it is almost impossible to perfectly model blood glucose dynamics while still being able to identify model parameters using only blood glucose measurements. The main contribution of this work is to propose a simple and identifiable linear dynamical model, which is based on the static prediction model of standard therapy. It is shown that the model parameters are intrinsically correlated with physician-set therapy parameters and that the reduction of the number of model parameters to identify leads to inferior data fits but to equivalent or slightly improved prediction capabilities compared to state-of-the-art models: a sign of an appropriate model structure and superior reliability. The validation of the proposed dynamic model is performed using data from the UVa simulator and real clinical data, and potential uses of the proposed model for state estimation and BG control are discussed. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.cmpb.2014.12.002 | Computer Methods and Programs in Biomedicine |
Keywords | Field | DocType |
Type 1 diabetes mellitus,Blood glucose prediction,Therapy parameters,Physiological model,Blood glucose control | Diabetes mellitus,Blood Glucose Measurement,Computer science,Correlation,Artificial intelligence,Type 1 diabetes,Surgery,Statistics,Insulin,Machine learning | Journal |
Volume | Issue | ISSN |
118 | 2 | 0169-2607 |
Citations | PageRank | References |
1 | 0.39 | 17 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alain Bock | 1 | 1 | 0.39 |
| Gregory François | 2 | 17 | 3.70 |
| Denis Gillet | 3 | 117 | 15.47 |