Name
Abstract | ||
|---|---|---|
This paper describes our preliminary steps towards the deployment of a brand-new original feature for a telemedicine portal aimed at helping people suffering from diabetes. In fact, people with diabetes necessitate careful handling of their disease to stay healthy. As such a disease is correlated to a malfunction of the pancreas that produces very little or no insulin, a way to enhance the quality of life of these subjects is to implement an artificial pancreas able to inject an insulin bolus when needed. The goal of this paper is to extrapolate a regression model, capable of estimating the blood glucose (BG) through interstitial glucose (IG) measurements, that represents a possible revolutionizing step in constructing the fundamental element of such an artificial pancreas. In particular, a new evolutionary approach is illustrated to stem a mathematical relationship between BG and IG. To accomplish the task, an automatic evolutionary procedure is also devised to estimate the missing BG values within the investigated real-world database made up of both BG and IG measurements of people suffering from Type 1 diabetes. The discovered model is validated through a comparison with other models during the experimental phase on global and personalized data treatment. Moreover, investigation is performed about the accuracy of one single global relationship model for all the subjects involved in the study, as opposed to that obtained through a personalized model found for each of them. Once this research is clinically validated, the important feature of estimating BG will be added to a web portal for diabetic subjects for telemedicine purposes. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.jnca.2018.06.007 | Journal of Network and Computer Applications |
Keywords | Field | DocType |
Blood glucose estimation,Interstitial glucose,Regression models,Evolutionary algorithms | Telemedicine,Artificial pancreas,Diabetes mellitus,Computer science,Genetic programming,Artificial intelligence,Type 1 diabetes,Machine learning,Distributed computing | Journal |
Volume | ISSN | Citations |
119 | 1084-8045 | 0 |
PageRank | References | Authors |
0.34 | 11 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ivanoe De Falco | 1 | 242 | 34.58 |
| Antonio Della Cioppa | 2 | 141 | 20.70 |
| Tomas Koutny | 3 | 8 | 5.36 |
| Michal Krcma | 4 | 1 | 1.72 |
| Umberto Scafuri | 5 | 116 | 16.33 |
| Ernesto Tarantino | 6 | 361 | 42.45 |