Deep Learning for Blood Glucose Prediction: CNN vs LSTM

2020 ◽  
pp. 379-393
Author(s):  
Touria El Idrissi ◽  
Ali Idri
Keyword(s):  
Deep Learning ◽  
Blood Glucose ◽  
Glucose Prediction

scholarly journals IoMT-Enabled Real-time Blood Glucose Prediction with Deep Learning and Edge Computing

2022 ◽  
pp. 1-1
Author(s):  
Taiyu Zhu ◽  
Lei Kuang ◽  
John Daniels ◽  
Pau Herrero ◽  
Kezhi Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Blood Glucose ◽  
Real Time ◽  
Edge Computing ◽  
Glucose Prediction

scholarly journals A Deep Learning Approach to Diabetic Blood Glucose Prediction

2017 ◽  
Vol 3 ◽  
Author(s):  
Hrushikesh N. Mhaskar ◽  
Sergei V. Pereverzyev ◽  
Maria D. van der Walt
Keyword(s):  
Deep Learning ◽  
Blood Glucose ◽  
Learning Approach ◽  
Glucose Prediction

scholarly journals Blood Glucose Prediction in Type 1 Diabetes Using Deep Learning on the Edge

2021 ◽  
Author(s):  
Taiyu Zhu ◽  
Lei Kuang ◽  
Kezhi Li ◽  
Junming Zeng ◽  
Pau Herrero ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Deep Learning ◽  
Blood Glucose ◽  
Glucose Prediction

scholarly journals A Model for Diabetic Blood Glucose Prediction Based on Electroencephalography Signals Using Deep Learning

2018 ◽  
Vol 12 ◽  
Author(s):  
Ali Berkol ◽  
Gokay Karayegen ◽  
Emre Tartan ◽  
Yahya Ekici ◽  
Gozde Kara ◽  
...  
Keyword(s):  
Deep Learning ◽  
Blood Glucose ◽  
Glucose Prediction

scholarly journals 90% Accuracy for Photoplethysmography-Based Non-Invasive Blood Glucose Prediction by Deep Learning with Cohort Arrangement and Quarterly Measured HbA1c

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7815
Author(s):  
Justin Chu ◽  
Wen-Tse Yang ◽  
Wei-Ru Lu ◽  
Yao-Ting Chang ◽  
Tung-Han Hsieh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Blood Glucose ◽  
Sample Size ◽  
Prediction Accuracy ◽  
Non Invasive ◽  
Physiological Diversity ◽  
Promising Solution ◽  
Glucose Prediction ◽  
Error Grid ◽  
Larger Sample

Previously published photoplethysmography-(PPG) based non-invasive blood glucose (NIBG) measurements have not yet been validated over 500 subjects. As illustrated in this work, we increased the number subjects recruited to 2538 and found that the prediction accuracy (the ratio in zone A of Clarke’s error grid) reduced to undesirable 60.6%. We suspect the low prediction accuracy induced by larger sample size might arise from the physiological diversity of subjects, and one possibility is that the diversity might originate from medication. Therefore, we split the subjects into two cohorts for deep learning: with and without medication (1682 and 856 recruited subjects, respectively). In comparison, the cohort training for subjects without any medication had approximately 30% higher prediction accuracy over the cohort training for those with medication. Furthermore, by adding quarterly (every 3 months) measured glycohemoglobin (HbA1c), we were able to significantly boost the prediction accuracy by approximately 10%. For subjects without medication, the best performing model with quarterly measured HbA1c achieved 94.3% prediction accuracy, RMSE of 12.4 mg/dL, MAE of 8.9 mg/dL, and MAPE of 0.08, which demonstrates a very promising solution for NIBG prediction via deep learning. Regarding subjects with medication, a personalized model could be a viable means of further investigation.

Deep Learning Applied to Blood Glucose Prediction from Flash Glucose Monitoring and Fitbit Data

2020 ◽  
pp. 59-63
Author(s):  
Pietro Bosoni ◽  
Marco Meccariello ◽  
Valeria Calcaterra ◽  
Cristiana Larizza ◽  
Lucia Sacchi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Blood Glucose ◽  
Glucose Monitoring ◽  
Flash Glucose Monitoring ◽  
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 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.

Sign in / Sign up

Export Citation Format

Share Document