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Ontological attention ensembles for capturing semantic concepts in ICD code prediction from clinical text

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Figure

Figure 1: Network architecture. The output of the convolutional module is fed into the ensemble of ancestralattention heads for multi-task learning
Table 3: Benchmark results for the models trained with F1micro stopping criterion.
Figure 3: F1macro for models using attention ensem-bles across different levels of the ontological tree
Figure 4: Two-dimensional t-SNE (Maaten and Hinton, 2008) representation of the ul vectors (which give rise tothe attention heads) for a subset of 182 codes with at least 100 occurrences each, in the data belonging to 16 differ-ent parent nodes
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