Unlocking the Power of Deep PICO Extraction: Step-wise Medical NER Identification
Table 3: Result of different DNER models.
['model', 'Precision', 'Recall', 'F1']
The performance of the DNER model has a major impact on the final results. We compare the performance of different models and the same model under different hyper-parameters. 100, optimizer=adam, char cnn embedding size=30, char LSTM embedding size=25, and mini batch size=32. We also use the BERT model which is the dominated model in various fields for medical entity recognition tasks. The experimental code refers to BERT-pytorch (https://github.com/codertimo/BERT-pytorch). Two different versions of BERT are implemented: BERT-base and BERT-large. With fewer parameters, BERT-base has faster training speed. but BERT-large achieves the best performance over all the other models with precision 0.8507, recall 0.8844 and F1 score 0.8672. For models with Bi-LSTM, our model Bi-LSTM + CRF + CNN char + graph embedding improves the baseline of F1 score from 80.16% to 85.08% and precision score from 79.10% to 84.33%. Compared to current state-of-the-art models, we achieve significant improvements.