Name
Abstract | ||
|---|---|---|
Diabetes has become one of the biggest health problems in the world. In this context, adherence to insulin treatment is essential in order to avoid life-threatening complications. In this pilot study, a novel adherence detection algorithm using Deep Learning (DL) approaches was developed for type 2 diabetes (T2D) patients, based on simulated Continuous Glucose Monitoring (CGM) signals. A large and diverse amount of CGM signals were simulated for T2D patients using a T2D adapted version of the Medtronic Virtual Patient (MVP) model for T1D. By using these signals, different classification algorithms were compared using a comprehensive grid search. We contrast a standard logistic regression baseline to Multi-Layer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). The best classification performance with an average accuracy of 77.5% was achieved with CNN. Hence, this indicates the potential of DL, when considering adherence detection systems for T2D patients. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/embc.2017.8037462 | 2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Field | DocType | Volume |
Hyperparameter optimization,Continuous glucose monitoring,Convolutional neural network,Computer science,Virtual patient,Artificial intelligence,Deep learning,Statistical classification,Perceptron,Logistic regression,Machine learning | Conference | 2017 |
ISSN | Citations | PageRank |
1094-687X | 0 | 0.34 |
References | Authors | |
6 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ali Mohebbi | 1 | 0 | 0.34 |
| Tinna B. Aradottir | 2 | 0 | 0.34 |
| Alexander R. Johansen | 3 | 0 | 0.34 |
| Henrik Bengtsson | 4 | 136 | 11.50 |
| Marco Fraccaro | 5 | 85 | 4.94 |
| Morten Mørup | 6 | 704 | 51.29 |