A function approximation approach to the prediction of blood glucose levels

TL;DR

This paper presents a novel function approximation method for real-time blood glucose level prediction using continuous glucose monitoring data, outperforming standard deep networks especially during abnormal glucose regimes.

Contribution

The paper introduces a new supervised function approximation approach leveraging manifold learning concepts for blood glucose prediction, handling distribution shifts between training and testing data.

Findings

Outperforms standard deep networks in hypoglycemic and hyperglycemic regimes.

Uses the PRED-EGA grid for clinically relevant evaluation.

Handles training and test data from different distributions.

Abstract

The problem of real time prediction of blood glucose (BG) levels based on the readings from a continuous glucose monitoring (CGM) device is a problem of great importance in diabetes care, and therefore, has attracted a lot of research in recent years, especially based on machine learning. An accurate prediction with a 30, 60, or 90 minute prediction horizon has the potential of saving millions of dollars in emergency care costs. In this paper, we treat the problem as one of function approximation, where the value of the BG level at time $t+h$ (where $h$ the prediction horizon) is considered to be an unknown function of $d$ readings prior to the time $t$. This unknown function may be supported in particular on some unknown submanifold of the $d$-dimensional Euclidean space. While manifold learning is classically done in a semi-supervised setting, where the entire data has to be known in advance, we use recent ideas to achieve an accurate function approximation in a supervised setting; i.e., construct a model for the target function. We use the state-of-the-art clinically relevant PRED-EGA grid to evaluate our results, and demonstrate that for a real life dataset, our method performs better than a standard deep network, especially in hypoglycemic and hyperglycemic regimes. One noteworthy aspect of this work is that the training data and test data may come from different distributions.