Bidirectional LSTM-CRF for Clinical Concept Extraction
Table 3: Performance comparison between the bidirectional LSTM CRF (bottom three lines) and state-of-the-art systems (top five lines) over the 2010 i2b2/VA concept extraction task.
['Methods', '[BOLD] 2010 i2b2/VA Precision', '[BOLD] 2010 i2b2/VA Recall', '[BOLD] 2010 i2b2/VA F1 Score']
However, our approach has achieved the second-best score on 2010 i2b2/VA. These results seem interesting on the ground that the bidirectional LSTM-CRF provide CE without utilizing any manually-engineered features. Given that our system learn entirely from the data, it is also robust to any new concept or unseen words additions. In our current experimental setting about 20% of tokens were either alpha-numeric or abbreviated strings whose Word2Vec or Glove pretrained vector embeddings were not available. These special strings in text were randomly initialized with d=300 vector embeddings and input to bidirectional LSTM-CRF system. Subsequently the system was able to learn meaningfull representations with remaining 80% of pre-trained vector embeddings and produce comparable results to the state-of-the-art CE systems.