Named Entity Recognition for Chinese Electronic Medical Records Based on Multitask and Transfer Learning

Abstract

Current work on named entities for Chinese electronic medical records requires training a separate model for each different type of electronic medical record, the performance of which depends on the amount of training data available for each dataset. However, different types of electronic medical records share similar semantic information with each other, while current models do not take full advantage of this potentially common knowledge. To overcome the mentioned problem, we propose a multi-task learning framework to transfer multiple types of electronic medical records through a shared encoder. Experiments demonstrate that our model achieves substantially better performance compared with the single-task model based on BERT. F1 scores improved by more than 1% on average across the four datasets, with individual datasets improving precision by more than 3.5%. Further analysis shows that our model still achieves better F1 scores on long tail datasets and small size datasets.

Uniting Multi-Scale Local Feature Awareness and the Self-Attention Mechanism for Named Entity Recognition

“…In the JNLPBA task, the method achieved an F1 score of 77.39%. Guo et al [34] proposed a method for named entity recognition of Chinese electronic medical records using multi-task learning and transfer learning. This method used a shared deep neural network to learn multiple related tasks, including disease and drug entity recognition, and used pre-training and fine-tuning for transfer learning to improve the model's generalization ability and robustness.…”