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arXiv · 1808.07380

On the Predictability of non-CGM Diabetes Data for Personalized Recommendation

Tu Nguyen·Markus Rokicki
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Abstract

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.

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Tu Nguyen, Markus Rokicki. 2018-08-19. On the Predictability of non-CGM Diabetes Data for Personalized Recommendation. https://arxiv.org/abs/1808.07380

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