A function approximation approach to the prediction of blood glucose levels
| Autor: | Hrushikesh N. Mhaskar, Sergei V. Pereverzyev, M. D. van der Walt |
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| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
FOS: Computer and information sciences Computer Science - Machine Learning Computer science Machine learning computer.software_genre supervised learning QA273-280 Machine Learning (cs.LG) hermite polynomial blood glucose prediction FOS: Mathematics Mathematics - Numerical Analysis T57-57.97 Applied mathematics. Quantitative methods Hermite polynomials Euclidean space business.industry Applied Mathematics Supervised learning Nonlinear dimensionality reduction Function (mathematics) Numerical Analysis (math.NA) prediction error-grid analysis Grid Function approximation continuous glucose monitoring Artificial intelligence business Probabilities. Mathematical statistics computer Test data |
| Zdroj: | Frontiers in Applied Mathematics and Statistics, Vol 7 (2021) |
| DOI: | 10.48550/arxiv.2105.05893 |
| Popis: | 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. Comment: arXiv admin note: text overlap with arXiv:1707.05828 |
| Databáze: | OpenAIRE |
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