Classifying the reported ability in clinical mobility descriptions
Table 6: Precision (Pr), Recall (Rec), and F1 for each model evaluated on the test set. Top rows are individual models, bottom rows are ensembled results. The best result in each column is marked in bold.
['System', 'Able Pr', 'Able Rec', 'Able F1', 'Unable Pr', 'Unable Rec', 'Unable F1', 'Unclear Pr', 'Unclear Rec', 'Unclear F1', 'None Pr', 'None Rec', 'None F1', 'Macro F1']
The ensembled systems achieve the best overall performance, with 77.4% macro F1 with the DNN chooser and 77.9% with majority voting. Due in large part to the class imbalance in the dataset, the SVM, CNN, and ensemble methods do not yield statistically significantly different results in most cases (p>0.001), although the voting ensemble does produce significantly higher precision on None samples than other methods (p≪0.001).