Figure 2.

Model performance on the test set by active learning batch, as measured by the mean of the multi-class scaled Brier scores for each frailty aspect (A). Error bars represent 95% confidence intervals. Elastic net regression had the best performance in each of the 5 rounds of active learning. Random forests also outperformed neural networks in each round. Multi-task neural networks outperformed single-task neural networks in all but the first and third rounds of active learning. Performance varied the most in early rounds when the training sample is the smallest. The cumulative number of sentences increased the most from active learning round 3 to active learning round 4 (B), corresponding to the largest increase in model performance.