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For people with type 1 diabetes (T1D), the beta cells in pancreas are destroyed and can not produce insulin which is the only hormone that response to high glucose concentrations. So, people with T1D must administer exogenous insulin to maintain their BGC within an euglycemic range. Hyperglycemia (high BGC) and hypoglycemia (low BGC) can occur because of poor BGC management. Using recursively identified models to predict the future BGC values opens novel possibilities for improving the BGC regulation performance by adjusting the dose of insulin infusion, taking rescue carbohydrates, or both. In this paper, a latent variable (LV) based multivariable statistical modeling approach is applied to model BGC dynamics and forecast future BGCs. The model is evaluated with the Ohio T1DM dataset that contains BGCs from continuous glucose monitoring (CGM) sensors, basal and bolus insulin information from insulin pump, and additional information from wristbands or reported by the subject. The average RMSE of 6 subjects is 19.37 mg/dL for 30-min-ahead prediction and 32.59 mg/dL for 60-min-ahead prediction. The results indicate that the LV-based model can predict future BGC values with high prediction accuracy.

ECAI 2020

Prediction of Glucose Levels for People with Type 1 Diabetes using Latent Variable based Model

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

technical paper

In this presentation, we show our results for the Blood Glucose Level Prediction Challenge for the Ohio2020 dataset. We have used four variants of genetic programming to build white-box models for predicting 30 minutes and 60 minutes ahead. The results are compared to classical methods including multivariate linear regression, random forests, as well as two types of ARIMA models. Notably, we have included future values of bolus and basal into some of the models because we assume that these values can be controlled. Additionally, we have used a convolution filter to smooth the information in the bolus volume feature. We find that overall tree-based GP per-forms well and better than multi-variate linear regression and random forest, while ARIMA models performed worst on the here analyzed data.

Analysis of the performance of Genetic Programming on the Blood Glucose Level Prediction Challenge 2020

"An essential part of this work is to provide a data-driven
model for predicting blood glucose levels that will help to warn the
patient about a potential hypo- or hyper-glycemic event in an easyto-
manage and discreet way."

A Deep Learning Approach for Blood Glucose Prediction of Type 1 Diabetes

"Real-time forecasting of blood glucose (BG) levels has
the potential to drastically improve management of Type 1 Diabetes,
a widespread chronic disease affecting the metabolic system. Most
notably, if hypo or hyperglycemia episodes (i.e. glycemic excursion
below or above a safe range) could be accurately predicted, then the
patient could be timely warned, thus enabling proactive countermeasures
to avoid these dangerous conditions. In this work, a novel personalized
algorithm for the real-time forecasting of BG is developed
by combining the output of a shallow feed forward neural network
with an error imputation module composed by an ensemble of trees.
Past glucose readings as well as insulin, meals and work/sleep time
information are carefully handled to train and boost the prediction
performance of the algorithm. The root mean square error over the 6
subjects achieves a mean value of 18.63 mg/dL and 32.27 mg/dL for
30- and 60-minute prediction horizon respectively."

Personalized Machine Learning Algorithm based on Shallow Network and Error Imputation Module for an Improved Blood Glucose Prediction

Accurate blood glucose (BG) prediction is necessary for daily glucose management of diabetes therapy. As glucose dynamics are often affected by various factors, such as diet, physical exercise, and insulin injection, it is difficult to consider all the relevant information and make a balance between the high-dimensional inputs and learning efficiency for a deep learning network. In this work, a novel multivariate predictor with a multi-scale long short-term memory (MS-LSTM) network was developed to automatically characterize the high-dimensional temporal dynamics and extract the features of blood glucose fluctuation and temporal trends sufficiently. Meanwhile, a multi-lag structure is designed for multiple variables, which can extract the dependence between different variables and blood glucose fluctuations more effectively. Furthermore, long-term sparse information was encoded and compressed to improve the learning efficiency of this deep learning network. The predictive capability of the proposed method was illustrated through 30-min and 60-min ahead glucose prediction in the OhioT1DM-2 Dataset. The root means square error (RMSE) values of 30-min and 60-min ahead predictions were 19.048 and 32.029, respectively, and the mean absolute error (MAE) values of 30-min and 60-min ahead predictions were 13.503 and 23.833. The results demonstrate the efficiency and prediction accuracy of the offline deep learning network, especially in the case of high-dimensional variables availability.

Multi-Scale Long Short-Term Memory Network with Multi-Lag Structure for Blood Glucose Prediction

Blood glucose (BG) prediction plays an important role in daily BG control. Accurate prediction of short-term glucose concentration can provide early warning for hyperglycemia and hypoglycemia events. This paper proposed a novel framework that combined an online prediction model with a residual compensation network. The autoregressive moving average (ARMA) model was used for online blood glucose prediction and the neural network was applied for compensation of prediction error. The advantages of this combined framework are: (1) the online ARMA model is efficient and robust to capture time-varying glucose dynamics, (2) the residual compensation network is capable to estimate errors from the online prediction model. The performance of this method was evaluated by the root mean squared error (RMSE) and the mean absolute error (MAE) in the dataset of OhioT1DM.The results were shown in detail that the mean values of the best RMSE of six patients at 30-min and 60-min horizon were 20.03 and 34.89 respectively, and the best MAE at 30-min and 60-min horizon were 14.52 and 24.61. Compared with the ARMA model, the combined predictor with a residual compensation network shows better prediction accuracy. Thus, we concluded that the proposed framework was an available approach for online blood glucose level prediction (BGLP).

Online Blood Glucose Prediction Using Autoregressive Moving Average Model with Residual Compensation Network

In this study we investigate the need for training future blood glucose level prediction models at the individual level (i.e. per patient). Specifically, we train various model classes: linear models, feed-forward neural networks, recurrent neural networks, and recurrent neural networks incorporating attention mechanisms, to predict future blood glucose levels using varying time series history lengths and data sources. We also compare methods of handling missing time series data during training. We found that relatively short history lengths provided the best results: a 30 minute history length proved optimal in our experiments. We observed long short-term memory (LSTM) networks performed better than linear and feed-forward neural networks, and that including an attention mechanism in the LSTM model further improved performance, even when processing sequences with relatively short length. We observed models trained using all of the available data outperformed those trained at the individual level. We also observed models trained using all of the available data, except for the data contributed by a given patient, were as effective at predicting the patient's future blood glucose levels as models trained using all of the available data. These models also significantly outperformed models trained using the patient's data only. Finally, we found that including sequences with missing values during training produced models that were more robust to missing values.

Experiments in non-personalised future blood glucose level prediction

This work suggests two methods—both relying on stacked regression and data fusion of CGM and activity—to predict the blood glucose level of patients with type 1 diabetes. Method 1 uses histories of CGM data appended with the average of activity data in the same histories to train three base regressions: a multilayer perceptron, a long short- term memory, and a partial least squares regression. In Method 2, histories of CGM and activity data are used separately to train the same base regressions. In both methods, the predictions from the base regressions are used as features to create a combined model. This model is then used to make the final predictions. The results obtained show the effectiveness of both methods. Method 1 provides slightly better results.

Data Fusion of Activity and CGM for Predicting Blood Glucose Levels

In the presentation, we explained the usage of Seq-2-Seq models for blood glucose levels prediction and explored the possibility of using Transfer Learning for the same purpose.  We provided a detailed explanation of the results from the corresponding paper.

Blood Glucose Level Prediction as TIme-Series Modeling using Sequence-to-Sequence Neural Networks

"Glucose control is an essential requirement in primary therapy for diabetes management. Digital approaches to maintaining tight glycaemic control, such as clinical decision support systems and artificial pancreas systems rely on continuous glucose monitoring devices and self-reported data, which is usually improved through glucose forecasting. In this work, we develop a multitask approach using
convolutional recurrent neural networks (MTCRNN) to provide short-term forecasts using the OhioT1DM dataset which comprises 12 participants. We obtain the following results - 30 min: 19.79 +/- 0.06 mg/dL (RMSE); 13.62 +/- 0.05 mg/dL (MAE) and 60 min: 33.73 +/- 0.24 mg/dL (RMSE); 24.54 +/- 0.15 mg/dL (MAE). Multitask learning facilitates an approach that allows for learning with the data from all available subjects, thereby overcoming the common challenge of insufficient individual datasets while learning appropriate individual models for each participant."

Personalised Glucose Prediction via Deep Multitask Networks

Control of blood glucose (BG) levels is essential for diabetes management, especially for long term health improvement. Predicting both hypoglycemic events (BG < 70 mg/dl) and hyperglycemic events (BG > 180 mg/dl) is essential in helping diabetics control their long term health. In this paper, we attempt to forecast future blood glucose levels, as well as analyze the efficiency of detecting both hypoglycemic events and hyperglycemic events. We do so by comparing Auto-Regressive Integrated Moving-Average, Vector Auto-Regression, Kalman Filter, Unscented Kalman Filter, Ordinary Least Squares, Support Vector Machines, Random Forests, Gradient Boosted Trees, XGBoosted Trees, Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multi-Layer Perceptron in terms of Root Mean Squared Error, Mean Absolute Error, Coefficient of Determination, Matthews Correlation Coefficient, and Clarke Error Grid to compare their effectiveness in predicting future blood glucose levels, as well as predicting both hypoglycemic and hyperglycemic events.

Prediction of Glucose Levels for People with Type 1 Diabetes using Latent Variable based Model

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