Name
Abstract | ||
|---|---|---|
Diabetes is an autoimmune disease characterized by glucose levels dysfunctions. It involves continuous monitoring combined with insulin treatment. Nowadays, continuous glucose monitoring systems (CGMs) have led to a greater availability of data. These can be effectively used by machine learning techniques to infer future values of the glycaemic concentration, allowing the early prevention of dangerous states and a better optimisation of the diabetic treatment. In this work, we investigate a patient-specialized prediction model. Thus, we designed a specialized solution based on Long Short-Term Memory (LSTM) neural network. Our solution was experimentally compared with two literature approaches, respectively based on Feed-Forward (FNN) and Recurrent (RNN) neural networks. The experimental results have highlighted that our LSTM solution obtained good performance both for short- and long-term glucose level inference (60 min.), overcoming the other methods both in terms of correlation between measured and predicted glucose signal and in terms of clinical outcome. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICMEW46912.2020.9105950 | 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) |
Keywords | DocType | ISSN |
CGM,Diabetes,Neural Networks,Time- series Analysis,Machine Learning,LSTM | Conference | 2330-7927 |
ISBN | Citations | PageRank |
978-1-7281-1486-6 | 0 | 0.34 |
References | Authors | |
7 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alessandro Aliberti | 1 | 1 | 1.36 |
| Andrea Bagatin | 2 | 0 | 0.34 |
| Andrea Acquaviva | 3 | 461 | 52.97 |
| Enrico Macii | 4 | 2405 | 349.96 |
| Edoardo Patti | 5 | 63 | 17.17 |