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

TL;DR

This paper introduces the Derivatives Combination Predictor (DCP), a novel model fusion algorithm that improves long-term glucose predictions for diabetics by enhancing clinical acceptability and response to glucose variation changes.

Contribution

The paper presents a new fusion algorithm with a specialized loss function that better captures glucose variations, tested on in-silico data with improved accuracy.

Findings

DCP outperforms other fusion algorithms in prediction accuracy.

The new loss function enhances the model's responsiveness to glucose changes.

DCP improves clinical safety and acceptability of glucose predictions.

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.