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

A function approximation approach to the prediction of blood glucose levels