Name
Title | ||
|---|---|---|
Minimizing postprandial hypoglycemia in Type 1 diabetes patients using multiple insulin injections and capillary blood glucose self-monitoring with machine learning techniques. |
Abstract | ||
|---|---|---|
•The methodology can be easily integrated in MDI insulin therapies based on SMBG.•Machine learning postprandial hypoglycemia prediction for a very challenging application: SMBG data.•More than 40% reduction in postprandial events of below 54 mg/dL hypoglycemia. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.cmpb.2019.06.025 | Computer Methods and Programs in Biomedicine |
Keywords | DocType | Volume |
Blood glucose,Bolus reduction,Hypoglycemia prediction,Machine learning,Postprandial hypoglycemia,Type 1 diabetes | Journal | 178 |
ISSN | Citations | PageRank |
0169-2607 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Silvia Oviedo | 1 | 3 | 1.75 |
| Ivan Contreras | 2 | 307 | 17.90 |
| Arthur Bertachi | 3 | 0 | 0.34 |
| Carmen Quirós | 4 | 3 | 1.09 |
| Marga Giménez | 5 | 4 | 1.47 |
| Ignacio Conget | 6 | 2 | 1.06 |
| Josep Vehi | 7 | 6 | 1.13 |