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.

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].

Data Driven Patient-Specialized Neural Networks for Blood Glucose Prediction

Aliberti, Alessandro;Acquaviva, Andrea;Macii, Enrico;Patti, Edoardo
2020

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.
2020
978-1-7281-1485-9
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2835725