On the Predictability of non-CGM Diabetes Data for Personalized Recommendation
- URL: http://arxiv.org/abs/1808.07380v5
- Date: Tue, 9 Apr 2024 14:40:13 GMT
- Title: On the Predictability of non-CGM Diabetes Data for Personalized Recommendation
- Authors: Tu Nguyen, Markus Rokicki,
- Abstract summary: 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.
We propose several post-prediction methods to account for the noise nature of these data, that marginally improves the performance of the end system.
- Score: 0.861716018559534
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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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