Fig. 7. Sensitivity analysis of the prediction horizon on three predictive scores with twofold mixup data augmentation on minority training data.

We compare the performance of our CNN model trained on the raw training dataset and on the training dataset augmented by two mixup models, one with α = 0.4 and the other with α = 2 for the Beta distribution Beta(α, α) implemented in mixup. The performance of each model is calibrated in terms of a prediction accuracy, b positive predictive value (PPV, the precision of the positive class), and c sensitivity (recall of the positive class). A table for the detailed numerical results is shown in Supplementary Table 6. Hypoglycemia (the minority class, also the positive class) samples in the training data is augmented with twofold mixup. The purple-shaded bars denote the predictive scores by mixup (α = 0.4), the red-shaded bars denote those by mixup (α = 2), and the gray-shaded bars denote those by the raw training data (no data augmentation). Error bars (standard deviation, s.d.) are computed over all patients’ results.