Name
Abstract | ||
|---|---|---|
•Risk-based postprandial hypoglycemia prediction is feasible for patients with Type 1 Diabetes using machine-learning techniques.•A high sensitivity and low false positive rate was obtained for Level 1 and Level 2 hypoglycemia using our methodology.•More than two thirds of the hypoglycemic events could be avoided thanks to our method.•The methodology can be easily integrated in platforms based on continuous glucose monitoring and intensive insulin management. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.ijmedinf.2019.03.008 | International Journal of Medical Informatics |
Keywords | Field | DocType |
Blood glucose,Bolus calculation,Hypoglycemia prediction,Machine learning,Postprandial hypoglycemia,Type 1 diabetes | Data mining,Internal medicine,Postprandial,Cardiology,Supervised learning,Postprandial Hypoglycemia,Insulin,Type 1 diabetes,Bolus (digestion),Cohort,Medicine,Hypoglycemia | Journal |
Volume | ISSN | Citations |
126 | 1386-5056 | 2 |
PageRank | References | Authors |
0.38 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Silvia Oviedo | 1 | 3 | 1.75 |
| Ivan Contreras | 2 | 307 | 17.90 |
| Carmen Quirós | 3 | 3 | 1.09 |
| Marga Giménez | 4 | 4 | 1.47 |
| Ignacio Conget | 5 | 2 | 1.06 |
| Josep Vehi | 6 | 6 | 1.13 |