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

被引:48
作者
Bertachi, Arthur [1 ,2 ]
Vinals, Clara [3 ]
Biagi, Lyvia [1 ,2 ]
Contreras, Ivan [1 ]
Vehi, Josep [1 ,4 ]
Conget, Ignacio [3 ,4 ]
Gimenez, Marga [3 ,4 ]
机构
[1] Univ Girona, Inst Informat & Applicat, Girona 17003, Spain
[2] Fed Univ Technol Parana UTFPR, BR-85053525 Guarapuava, Brazil
[3] Hosp Clin Barcelona, Diabet Unit, Endocrinol & Nutr Dept, E-08036 Barcelona, Spain
[4] Ctr Invest Biomed Red Diabet & Enfermedades Metab, Barcelona 08036, Spain
关键词
artificial neural network; hypoglycemia; machine learning; support vector machine; type; 1; diabetes; multiple daily injections; continuous glucose monitoring; INSULIN PUMP THERAPY; GLYCEMIC CONTROL; CHILDREN; ADOLESCENTS; INTENSITY; IMPACT;
D O I
10.3390/s20061705
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
(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.
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页数:11
相关论文
共 43 条
[11]   Insulin Pump Therapy With Automated Insulin Suspension in Response to Hypoglycemia Reduction in nocturnal hypoglycemia in those at greatest risk [J].
Choudhary, Pratik ;
Shin, John ;
Wang, Yongyin ;
Evans, Mark L. ;
Hammond, Peter J. ;
Kerr, David ;
Shaw, James A. M. ;
Pickup, John C. ;
Amiel, Stephanie A. .
DIABETES CARE, 2011, 34 (09) :2023-2025
[12]   Artificial Intelligence for Diabetes Management and Decision Support: Literature Review [J].
Contreras, Ivan ;
Vehi, Josep .
JOURNAL OF MEDICAL INTERNET RESEARCH, 2018, 20 (05)
[13]   Personalized blood glucose prediction: A hybrid approach using grammatical evolution and physiological models [J].
Contreras, Ivan ;
Oviedo, Silvia ;
Vettoretti, Martina ;
Visentin, Roberto ;
Vehi, Josep .
PLOS ONE, 2017, 12 (11)
[14]   Health-related quality of life associated with daytime and nocturnal hypoglycaemic events: a time trade-off survey in five countries [J].
Evans, Marc ;
Khunti, Kamlesh ;
Mamdani, Muhammad ;
Galbo-Jorgensen, Claus B. ;
Gundgaard, Jens ;
Bogelund, Mette ;
Harris, Stewart .
HEALTH AND QUALITY OF LIFE OUTCOMES, 2013, 11
[15]   How hypoglycaemia can affect the life of a person with diabetes [J].
Frier, Brian M. .
DIABETES-METABOLISM RESEARCH AND REVIEWS, 2008, 24 (02) :87-92
[16]   Hypoglycaemia in diabetes mellitus: epidemiology and clinical implications [J].
Frier, Brian M. .
NATURE REVIEWS ENDOCRINOLOGY, 2014, 10 (12) :711-722
[17]  
Georga EI, 2012, IEEE ENG MED BIO, P2889, DOI 10.1109/EMBC.2012.6346567
[18]   The risks of nocturnal hypoglycaemia in insulin-treated diabetes [J].
Graveling, Alex J. ;
Frier, Brian M. .
DIABETES RESEARCH AND CLINICAL PRACTICE, 2017, 133 :30-39
[19]   Nocturnal hypoglycaemias in type 1 diabetic patients: what can we learn with continuous glucose monitoring? [J].
Guillod, L. ;
Comte-Perret, S. ;
Monbaron, D. ;
Gaillard, R. C. ;
Ruiz, J. .
DIABETES & METABOLISM, 2007, 33 (05) :360-365
[20]   Impact of Sensor-Augmented Pump Therapy with Predictive Low-Glucose Suspend Function on Glycemic Control and Patient Satisfaction in Adults and Children with Type 1 Diabetes [J].
Isabel Beato-Vibora, Pilar ;
Quiros-Lopez, Carmen ;
Lazaro-Martin, Lucia ;
Martin-Frias, Maria ;
Barrio-Castellanos, Raquel ;
Gil-Poch, Estela ;
Javier Arroyo-Diez, Francisco ;
Gimenez-Alvarez, Marga .
DIABETES TECHNOLOGY & THERAPEUTICS, 2018, 20 (11) :738-743