Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network

Sun, Qingnan; Janković, Marko; Bally, Lia; Mougiakakou, Stavroula

doi:10.1109/neurel.2018.8586990

Cited by 112 publications

(99 citation statements)

References 12 publications

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“…For datasets generated from the simulator, our proposed method is better than [33] (RMSE = 18.8), [34] (RMSE = 13.7). For real data, GluNet's RMSE 19.2 is better than 23.4 in [35], and 21.8 in [36]. GluNet's time lag 11.3 mins is much better than 20.4 mins in [36] for 30 mins PH.…”

Section: B Comparison With Other Existing Algorithmsmentioning

confidence: 89%

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GluNet: A Deep Learning Framework for Accurate Glucose Forecasting

Liu

Zhu

et al. 2020

IEEE J. Biomed. Health Inform.

121

115

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For people with Type 1 diabetes (T1D), forecasting of blood glucose (BG) can be used to effectively avoid hyperglycemia, hypoglycemia and associated complications. The latest continuous glucose monitoring (CGM) technology allows people to observe glucose in real-time. However, an accurate glucose forecast remains a challenge. In this work, we introduce GluNet, a framework that leverages on a personalized deep neural network to predict the probabilistic distribution of short-term (30-60 minutes) future CGM measurements for subjects with T1D based on their historical data including glucose measurements, meal information, insulin doses, and other factors. It adopts the latest deep learning techniques consisting of four components: data pre-processing, label transform/recover, multi-layers of dilated convolution neural network (CNN), and post-processing. The method is evaluated in-silico for both adult and adolescent subjects. The results show significant improvements over existing methods in the literature through a comprehensive comparison in terms of root mean square error (RMSE) (8.88 ± 0.77 mg/dL) with short time lag (0.83 ± 0.40 minutes) for prediction horizons (PH) = 30 mins (minutes), and RMSE (19.90 ± 3.17 mg/dL) with time lag (16.43 ± 4.07 mins) for PH = 60 mins for virtual adult subjects. In addition, GluNet is also tested on two clinical data sets. Results show that it achieves an RMSE (19.28±2.76 mg/dL) with time lag (8.03 ± 4.07 mins) for PH = 30 mins and an RMSE (31.83±3.49 mg/dL) with time lag (17.78±8.00 mins) for PH = 60 mins. These are the best reported results for glucose forecasting when compared with other methods including the neural network for predicting glucose (NNPG), the support vector regression (SVR), the latent variable with exogenous input (LVX), and the auto regression with exogenous input (ARX) algorithm.

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Section: B Comparison With Other Existing Algorithmsmentioning

confidence: 89%

“…For real data, GluNet's RMSE 19.2 is better than 23.4 in [35], and 21.8 in [36]. GluNet's time lag 11.3 mins is much better than 20.4 mins in [36] for 30 mins PH. However, due to the unavailability of the original codes and other paper's RMSE, it is not easy to have a fair comparison.…”

Section: B Comparison With Other Existing Algorithmsmentioning

confidence: 89%

GluNet: A Deep Learning Framework for Accurate Glucose Forecasting

Liu

Zhu

et al. 2020

IEEE J. Biomed. Health Inform.

121

115

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“…According to El Idrissi et al [7], considerable work was done for the BGL prediction and various Data Mining approaches including statistical methods and machine learning techniques were investigated for that purpose; the most used ones are Artificial Neural Networks (NNs) and Auto Regression (AR) [7]. Recently, deep learning modeling is gaining more interest, such as LSTM NN [8] and deep NN [9]. This paper proposes a deep learning NN with one LSTM layer and two fully connected layers for the prediction of BGL using CGM data.…”

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confidence: 99%

Predicting blood glucose using an LSTM Neural Network

Idriss¹,

Idri

Abnane

et al. 2019

Annals of Computer Science and Information Systems

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Diabetes self-management relies on the blood glucose prediction as it allows taking suitable actions to prevent low or high blood glucose level. In this paper, we propose a deep learning neural network (NN) model for blood glucose prediction. It is a sequential one using a Long-Short-Term Memory (LSTM) layer with two fully connected layers. Several experiments were carried out over data of 10 diabetic patients to decide on the model's parameters in order to identify the best variant of it. The performance of the proposed LSTM NN measured in terms of root mean square error (RMSE) was compared with the ones of an existing LSTM and an autoregressive (AR) models. The results show that our LSTM NN is significantly more accurate; in fact, it outperforms the existing LSTM model for all patients and outperforms the AR model in 9 over 10 patients, besides, the performance differences were assessed by the Wilcoxon statistical test. Furthermore, the mean of the RMSE of our model was 12.38 mg/dl while it was 28.84 mg/dl and 50.69 mg/dl for AR and the existing LSTM respectively.

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“…In the past few years, a lot of different glucose predictive model architectures have been tried-out. Among them, Sun et al proposed a generic predictive model using Long Short-Term Memory (LSTM) and bidirectional LSTM neural networks to predict glucose at prediction horizons (PH) up to 60 minutes [6]. De Paula et al studied the use of Gaussian Processes (GP) to predict future glucose values in an automated glucose controller based on reinforcement learning [7].…”

Section: Introductionmentioning

confidence: 99%

Model Fusion to Enhance the Clinical Acceptability of Long-Term Glucose Predictions

Bois

Ammi²,

Yacoubi

2019

2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)

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This paper presents the Derivatives Combination Predictor (DCP), a novel model fusion algorithm for making long-term glucose predictions for diabetic people. First, using the history of glucose predictions made by several models, the future glucose variation at a given horizon is predicted. Then, by accumulating the past predicted variations starting from a known glucose value, the fused glucose prediction is computed. A new loss function is introduced to make the DCP model learn to react faster to changes in glucose variations. The algorithm has been tested on 10 in-silico type-1 diabetic children from the T1DMS software. Three initial predictors have been used: a Gaussian process regressor, a feed-forward neural network and an extreme learning machine model. The DCP and two other fusion algorithms have been evaluated at a prediction horizon of 120 minutes with the root-mean-squared error of the prediction, the root-mean-squared error of the predicted variation, and the continuous glucose-error grid analysis. By making a successful trade-off between prediction accuracy and predicted-variation accuracy, the DCP, alongside with its specifically designed loss function, improves the clinical acceptability of the predictions, and therefore the safety of the model for diabetic people.

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Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network

Cited by 112 publications

References 12 publications

GluNet: A Deep Learning Framework for Accurate Glucose Forecasting

GluNet: A Deep Learning Framework for Accurate Glucose Forecasting

Predicting blood glucose using an LSTM Neural Network

Model Fusion to Enhance the Clinical Acceptability of Long-Term Glucose Predictions

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