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Open AccessArticle

Prediction of Nocturnal Hypoglycemia in Adults with Type 1 Diabetes under Multiple Daily Injections Using Continuous Glucose Monitoring and Physical Activity Monitor

1
Institute of Informatics and Applications, University of Girona, 17003 Girona, Spain
2
Federal University of Technology—Paraná (UTFPR), Guarapuava 85053-525, Brazil
3
Diabetes Unit, Endocrinology and Nutrition Dpt. Hospital Clínic de Barcelona, 08036 Barcelona, Spain
4
Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), 08036 Barcelona, Spain
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2020, 20(6), 1705; https://doi.org/10.3390/s20061705
Received: 25 February 2020 / Revised: 10 March 2020 / Accepted: 17 March 2020 / Published: 19 March 2020
(This article belongs to the Special Issue Sensor Technologies: Artificial Intelligence for Diabetes Management)
(1) Background: nocturnal hypoglycemia (NH) is one of the most challenging side effects of multiple doses of insulin (MDI) therapy in type 1 diabetes (T1D). This work aimed to investigate the feasibility of a machine-learning-based prediction model to anticipate NH in T1D patients on MDI. (2) Methods: ten T1D adults were studied during 12 weeks. Information regarding T1D management, continuous glucose monitoring (CGM), and from a physical activity tracker were obtained under free-living conditions at home. Supervised machine-learning algorithms were applied to the data, and prediction models were created to forecast the occurrence of NH. Individualized prediction models were generated using multilayer perceptron (MLP) and a support vector machine (SVM). (3) Results: population outcomes indicated that more than 70% of the NH may be avoided with the proposed methodology. The predictions performed by the SVM achieved the best population outcomes, with a sensitivity and specificity of 78.75% and 82.15%, respectively. (4) Conclusions: our study supports the feasibility of using ML techniques to address the prediction of nocturnal hypoglycemia in the daily life of patients with T1D on MDI, using CGM and a physical activity tracker. View Full-Text
Keywords: artificial neural network; hypoglycemia; machine learning; support vector machine; type 1 diabetes; multiple daily injections; continuous glucose monitoring artificial neural network; hypoglycemia; machine learning; support vector machine; type 1 diabetes; multiple daily injections; continuous glucose monitoring
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MDPI and ACS Style

Bertachi, A.; Viñals, C.; Biagi, L.; Contreras, I.; Vehí, J.; Conget, I.; Giménez, M. Prediction of Nocturnal Hypoglycemia in Adults with Type 1 Diabetes under Multiple Daily Injections Using Continuous Glucose Monitoring and Physical Activity Monitor. Sensors 2020, 20, 1705. https://doi.org/10.3390/s20061705

AMA Style

Bertachi A, Viñals C, Biagi L, Contreras I, Vehí J, Conget I, Giménez M. Prediction of Nocturnal Hypoglycemia in Adults with Type 1 Diabetes under Multiple Daily Injections Using Continuous Glucose Monitoring and Physical Activity Monitor. Sensors. 2020; 20(6):1705. https://doi.org/10.3390/s20061705

Chicago/Turabian Style

Bertachi, Arthur; Viñals, Clara; Biagi, Lyvia; Contreras, Ivan; Vehí, Josep; Conget, Ignacio; Giménez, Marga. 2020. "Prediction of Nocturnal Hypoglycemia in Adults with Type 1 Diabetes under Multiple Daily Injections Using Continuous Glucose Monitoring and Physical Activity Monitor" Sensors 20, no. 6: 1705. https://doi.org/10.3390/s20061705

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