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arXiv · 2009.04410

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

Maxime De Bois·Mounîm A. El Yacoubi·Mehdi Ammi
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Abstract

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.

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Maxime De Bois, Mounîm A. El Yacoubi, Mehdi Ammi. 2020-09-08. Model Fusion to Enhance the Clinical Acceptability of Long-Term Glucose Predictions. https://doi.org/10.1109/bibe.2019.00053

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