With continuous glucose monitoring (CGM), data-driven models on blood glucose prediction have been shown to be effective in related work. However, such (CGM) systems are not always available, e.g., for a patient at home. In this work, we conduct a study on 9 patients and examine the online predictability of data-driven (aka. machine learning) based models on patient-level blood glucose prediction; with measurements are taken only periodically (i.e., after several hours). To this end, we propose several post-prediction methods to account for the noise nature of these data, that marginally improves the performance of the end system.
@article{arxiv.1808.07380,
title = {On the Predictability of non-CGM Diabetes Data for Personalized Recommendation},
author = {Tu Nguyen and Markus Rokicki},
journal= {arXiv preprint arXiv:1808.07380},
year = {2024}
}