TY - GEN
T1 - Deep Learning Applied to Blood Glucose Prediction from Flash Glucose Monitoring and Fitbit Data
AU - Bosoni, Pietro
AU - Meccariello, Marco
AU - Calcaterra, Valeria
AU - Larizza, Cristiana
AU - Sacchi, Lucia
AU - Bellazzi, Riccardo
PY - 2020
Y1 - 2020
N2 - Blood glucose (BG) monitoring devices play an important role in diabetes management, offering real time BG measurements, which can be analyzed to discover new knowledge. In this paper we present a multi-patient and multivariate deep learning approach, based on Long-Short Term Memory (LSTM) artificial neural networks, for building a generalized model to forecast BG levels on a short-time prediction horizon. The proposed framework is evaluated on a clinical dataset of 17 patients, receiving care at the IRCCS Policlinico San Matteo hospital in Pavia, Italy. BG profiles collected by a flash glucose monitoring system were analyzed together with information collected by an activity tracker, including heart rate, sleep, and physical activity. Results suggest that a model with good prediction performance can be obtained and that a combination of HR and lifestyle monitoring signals can help to predict BG levels.
AB - Blood glucose (BG) monitoring devices play an important role in diabetes management, offering real time BG measurements, which can be analyzed to discover new knowledge. In this paper we present a multi-patient and multivariate deep learning approach, based on Long-Short Term Memory (LSTM) artificial neural networks, for building a generalized model to forecast BG levels on a short-time prediction horizon. The proposed framework is evaluated on a clinical dataset of 17 patients, receiving care at the IRCCS Policlinico San Matteo hospital in Pavia, Italy. BG profiles collected by a flash glucose monitoring system were analyzed together with information collected by an activity tracker, including heart rate, sleep, and physical activity. Results suggest that a model with good prediction performance can be obtained and that a combination of HR and lifestyle monitoring signals can help to predict BG levels.
KW - Data integration
KW - Deep learning
KW - Diabetes
KW - Flash glucose monitoring
KW - Time series analysis
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U2 - 10.1007/978-3-030-59137-3_6
DO - 10.1007/978-3-030-59137-3_6
M3 - Conference contribution
AN - SCOPUS:85092229665
SN - 9783030591366
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 59
EP - 63
BT - Artificial Intelligence in Medicine - 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Proceedings
A2 - Michalowski, Martin
A2 - Moskovitch, Robert
PB - Springer Science and Business Media Deutschland GmbH
T2 - 18th International Conference on Artificial Intelligence in Medicine, AIME 2020
Y2 - 25 August 2020 through 28 August 2020
ER -