Enhancing Clinical Concept Extraction with Contextual Embeddings
Table 4: Performance of each label category with pre-trained MIMIC models on i2b2 2010 task.
['2.5', '[BOLD] word2vec', '[BOLD] GloVe', '[BOLD] fastText', '[BOLD] ELMo', '[BOLD] BERTBASE', '[BOLD] BERTLARGE']
Both ELMo and BERT show improvements to all three categories, with ELMo outperforming the traditional embeddings on all three, and BERT outperforming ELMo on all three. One notable aspect with BERT is that Treatments see a larger jump: Treatment is the lowest-performing category for ELMo and the traditional embeddings despite there being slightly more Treatments than Tests in the data, but for BERT the Treatments category outperforms Tests.