  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.
