Blood glucose prediction using neural network

2008 ◽  
Author(s):  
Chit Siang Soh ◽  
Xiqin Zhang ◽  
Jianhong Chen ◽  
P. Raveendran ◽  
Phey Hong Soh ◽  
...  
Keyword(s):  
Neural Network ◽  
Blood Glucose ◽  
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.

Blood glucose prediction model for type 1 diabetes based on artificial neural network with time-domain features

2020 ◽  
Vol 40 (4) ◽  
pp. 1586-1599
Author(s):  
Ganjar Alfian ◽  
Muhammad Syafrudin ◽  
Muhammad Anshari ◽  
Filip Benes ◽  
Fransiskus Tatas Dwi Atmaji ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Prediction Model ◽  
Time Domain ◽  
Artificial Neural ◽  
Glucose Prediction

scholarly journals Developing an Individual Glucose Prediction Model Using Recurrent Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6460
Author(s):  
Dae-Yeon Kim ◽  
Dong-Sik Choi ◽  
Jaeyun Kim ◽  
Sung Wan Chun ◽  
Hyo-Wook Gil ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Prediction Model ◽  
Glucose Level ◽  
Recurrent Neural Network ◽  
Learning Algorithm ◽  
Percentage Error ◽  
Glucose Prediction

In this study, we propose a personalized glucose prediction model using deep learning for hospitalized patients who experience Type-2 diabetes. We aim for our model to assist the medical personnel who check the blood glucose and control the amount of insulin doses. Herein, we employed a deep learning algorithm, especially a recurrent neural network (RNN), that consists of a sequence processing layer and a classification layer for the glucose prediction. We tested a simple RNN, gated recurrent unit (GRU), and long-short term memory (LSTM) and varied the architectures to determine the one with the best performance. For that, we collected data for a week using a continuous glucose monitoring device. Type-2 inpatients are usually experiencing bad health conditions and have a high variability of glucose level. However, there are few studies on the Type-2 glucose prediction model while many studies performed on Type-1 glucose prediction. This work has a contribution in that the proposed model exhibits a comparative performance to previous works on Type-1 patients. For 20 in-hospital patients, we achieved an average root mean squared error (RMSE) of 21.5 and an Mean absolute percentage error (MAPE) of 11.1%. The GRU with a single RNN layer and two dense layers was found to be sufficient to predict the glucose level. Moreover, to build a personalized model, at most, 50% of data are required for training.

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.

Sign in / Sign up

Export Citation Format

Share Document