Research on Relation Extraction Method of Chinese Electronic Medical Records Based on BERT

Abstract

Relation extraction is a necessary step in obtaining information from electronic medical records. The deep learning methods for relation extraction are primarily based on word2vec and convolutional or recurrent neural network. However, word vectors generated by word2vec are static and cannot well reflect the different meanings of polysemy in different contexts and the feature extraction ability of RNN (Recurrent Neural Network) is not good enough. At the same time, the BERT (Bidirectional Encoder Representations from Transformers) pre-trained language model has achieved excellent results in many natural language processing tasks. In this paper, we propose a medical relation extraction model based on BERT. We combine the information of the whole sentence obtained from the pre-train language model with the corresponding information of two medical entities to complete relation extraction task. The experimental data were obtained from the Chinese electronic medical records provided by a hospital in Beijing. Experimental results on electronic medical records show that our model's accuracy, precision, recall, and F1-score reach 67.37%, 69.54%, 67.38%, 68.44%, which are higher than other three methods. Because named entity recognition task is the premise of relation extraction, we will combine the model with named entity recognition in the future work.