  Due to the sensitive nature of diabetes-related data, preventing them from
being shared between studies, progress in the field of glucose prediction is
hard to assess. To address this issue, we present GLYFE (GLYcemia Forecasting
Evaluation), a benchmark of machine-learning-based glucose-predictive models.
  To ensure the reproducibility of the results and the usability of the
benchmark in the future, we provide extensive details about the data flow. Two
datasets are used, the first comprising 10 in-silico adults from the UVA/Padova
Type 1 Diabetes Metabolic Simulator (T1DMS) and the second being made of 6 real
type-1 diabetic patients coming from the OhioT1DM dataset. The predictive
models are personalized to the patient and evaluated on 3 different prediction
horizons (30, 60, and 120 minutes) with metrics assessing their accuracy and
clinical acceptability.
  The results of nine different models coming from the glucose-prediction
literature are presented. First, they show that standard autoregressive linear
models are outclassed by kernel-based non-linear ones and neural networks. In
particular, the support vector regression model stands out, being at the same
time one of the most accurate and clinically acceptable model. Finally, the
relative performances of the models are the same for both datasets. This shows
that, even though data simulated by T1DMS are not fully representative of
real-world data, they can be used to assess the forecasting ability of the
glucose-predictive models.
  Those results serve as a basis of comparison for future studies. In a field
where data are hard to obtain, and where the comparison of results from
different studies is often irrelevant, GLYFE gives the opportunity of gathering
researchers around a standardized common environment.
