Short and Medium Term Blood Glucose Prediction Using Multi-objective Grammatical Evolution

2020 ◽  
pp. 494-509
Author(s):  
Sergio Contador ◽  
J. Manuel Colmenar ◽  
Oscar Garnica ◽  
J. Ignacio Hidalgo
Keyword(s):  
Blood Glucose ◽  
Grammatical Evolution ◽  
Medium Term ◽  
Multi Objective ◽  
Glucose Prediction

scholarly journals Personalized blood glucose prediction: A hybrid approach using grammatical evolution and physiological models

PLoS ONE ◽  
2017 ◽  
Vol 12 (11) ◽  
pp. e0187754 ◽  
Author(s):  
Iván Contreras ◽  
Silvia Oviedo ◽  
Martina Vettoretti ◽  
Roberto Visentin ◽  
Josep Vehí
Keyword(s):  
Blood Glucose ◽  
Hybrid Approach ◽  
Grammatical Evolution ◽  
Physiological Models ◽  
Glucose Prediction

Examining Sensor Agreement in Neural Network Blood Glucose Prediction

2021 ◽  
pp. 193229682110182
Author(s):  
Aaron P. Tucker ◽  
Arthur G. Erdman ◽  
Pamela J. Schreiner ◽  
Sisi Ma ◽  
Lisa S. Chow
Keyword(s):  
Neural Network ◽  
Blood Glucose ◽  
Patient Specific ◽  
Sensor Location ◽  
Effective Care ◽  
Freestyle Libre ◽  
Patients With Diabetes ◽  
Interstitial Glucose ◽  
Continuous Glucose Monitors ◽  
Glucose Prediction

Successful measurements of interstitial glucose are a key component in providing effective care for patients with diabetes. Recently, there has been significant interest in using neural networks to forecast future glucose values from interstitial measurements collected by continuous glucose monitors (CGMs). While prediction accuracy continues to improve, in this work we investigated the effect of physiological sensor location on neural network blood glucose forecasting. We used clinical data from patients with Type 2 Diabetes who wore blinded FreeStyle Libre Pro CGMs (Abbott) on both their right and left arms continuously for 12 weeks. We trained patient-specific prediction algorithms to test the effect of sensor location on neural network forecasting ( N = 13, Female = 6, Male = 7). In 10 of our 13 patients, we found at least one significant ( P < .05) increase in forecasting error in algorithms which were tested with data taken from a different location than data which was used for training. These reported results were independent from other noticeable physiological differences between subjects (eg, height, age, weight, blood pressure) and independent from overall variance in the data. From these results we observe that CGM location can play a consequential role in neural network glucose prediction.

scholarly journals The Research of Improved Grey GM (1, 1) Model to Predict the Postprandial Glucose in Type 2 Diabetes

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Yannian Wang ◽  
Fenfen Wei ◽  
Changqing Sun ◽  
Quanzhong Li
Keyword(s):  
Type 2 Diabetes ◽  
Blood Glucose ◽  
Prediction Model ◽  
Glucose Concentration ◽  
Blood Glucose Concentration ◽  
Original Data ◽  
Postprandial Glucose ◽  
Postprandial Blood Glucose ◽  
Glucose Prediction

Diabetes may result in some complications and increase the risk of many serious health problems. The purpose of clinical treatment is to carefully manage the blood glucose concentration. If the blood glucose concentration is predicted, treatments can be taken in advance to reduce the harm to patients. For this purpose, an improved grey GM (1, 1) model is applied to predict blood glucose with a small amount of data, and especially in terms of improved smoothness it can get higher prediction accuracy. The original data of blood glucose of type 2 diabetes is acquired by CGMS. Then the prediction model is established. Finally, 50 cases of blood glucose from the Henan Province People’s Hospital are predicted in 5, 10, 15, 20, 25, and 30 minutes, respectively, in advance to verify the prediction model. The prediction result of blood glucose is evaluated by the EGA, MSE, and MAE. Particularly, the prediction results of postprandial blood glucose are presented and analyzed. The result shows that the improved grey GM (1, 1) model has excellent performance in postprandial blood glucose prediction.

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