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EnglishDiabetes 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.
Data Driven Patient-Specialized Neural Networks for Blood Glucose Prediction / Aliberti, Alessandro; Bagatin, Andrea; Acquaviva, Andrea; Macii, Enrico; Patti, Edoardo. - (2020), pp. 1-6. ((Intervento presentato al convegno 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW 2020) tenutosi a London, United Kingdom, United Kingdom nel 6-10 July 2020 [10.1109/ICMEW46912.2020.9105950].
| Titolo: | Data Driven Patient-Specialized Neural Networks for Blood Glucose Prediction | |
| Autori: | ||
| Data di pubblicazione: | 2020 | |
| Abstract: | Diabetes is an autoimmune disease characterized by glucose levels dysfunctions. It involves conti...nuous 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. | |
| ISBN: | 978-1-7281-1485-9 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| main.pdf | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri | |
| 09105950.pdf | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
http://hdl.handle.net/11583/2835725
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