Figure 2.

(A) Finger-prick glucose measurements and meal carbohydrates serve as the training data (one segment shown) with a goal to predict glucose in the future. (B) DA’s point-wise forecasts seem reasonable but not perfect, predicting one spike but missing another. (C) Underlying continuous DA forecast with uncertainty quantification (which the point-wise forecasts are based on) appears to overfit the data with large glucose swings. (D) Continuous glucose monitoring, which was hidden from the DA, reveals striking overlap between the continuous DA predictions and the actual glucose levels. Despite insufficient information in the training set, the DA tracked glucose well based on the combination of its glucose metabolism constraints and the sparse measurements.