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

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 \textit{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

Study of Short-Term Personalized Glucose Predictive Models on Type-1 Diabetic Children

Research in diabetes, especially when it comes to building data-driven models to forecast future glucose values, is hindered by the sensitive nature of the data. Because researchers do not share the same data between studies, progress is hard to assess. This paper aims at comparing the most promising algorithms in the field, namely Feedforward Neural Networks (FFNN), Long Short-Term Memory (LSTM) Recurrent Neural Networks, Extreme Learning Machines (ELM), Support Vector Regression (SVR) and Gaussian Processes (GP). They are personalized and trained on a population of 10 virtual children from the Type 1 Diabetes Metabolic Simulator software to predict future glucose values at a prediction horizon of 30 minutes. The performances of the models are evaluated using the Root Mean Squared Error (RMSE) and the Continuous Glucose-Error Grid Analysis (CG-EGA). While most of the models end up having low RMSE, the GP model with a Dot-Product kernel (GP-DP), a novel usage in the context of glucose prediction, has the lowest. Despite having good RMSE values, we show that the models do not necessarily exhibit a good clinical acceptability, measured by the CG-EGA. Only the LSTM, SVR and GP-DP models have overall acceptable results, each of them performing best in one of the glycemia regions

Estimating spatial and temporal variations in solar radiation within Bordeaux winegrowing region using remotely sensed data

International audienceAims: This paper presents a study solar radiation spatial and temporal variations in Bordeaux winegrowing area, for a 20 year period (1986-2005). Methods and results: Solar radiation data was retrieved from the HelioClim-1 database, elaborated from Meteosat satellite images, using the Heliosat-2 algorithm. Daily data was interpolated using ordinary kriging to produce horizontal solar radiation maps at a 500 m resolution. Using a digital elevation model, high resolution daily solar radiation maps with terrain integration were then produced for the period 2001-2005, at a 50 m resolution. The long term (20 years) analysis of solar radiation at low spatial resolution (500 m) showed a west to east decreasing gradient within Bordeaux vineyards. Mean August-to-September daily irradiation values, on horizontal surface, were used to classify Bordeaux winegrowing areas in three zones: low, medium, and high solar radiation areas. This initial zoning was upscaled at 50 m resolution, applying a local correction ratio, based on 2001-2005 solar radiation on inclined surface analysis. Grapevine development and maturation potential of the different zones of appellation of origin of Bordeaux winegrowing area are discussed in relation with this zoning. 2 Conclusions: Solar radiation variability within Bordeaux winegrowing area is mainly governed by terrain slopes and orientations, which induce considerable variations within the eastern part of Bordeaux vineyards. Significance and impact of the study: Solar radiation has a major impact on vineyard water balance, grapevine development and berry ripening. However, irradiation data is seldom available in weather stations records. This paper underline the interest of high resolution cartography of solar radiation, using satellite sensing and terrain effect integration, for agroclimatic studies in viticulture