A BiLSTM-CRF Method to Chinese Electronic Medical Record Named Entity Recognition

Abstract

With the application of electronic medical records in medical field, more and more people are paying attention to how to use these data efficiently. In this paper, the BiLSTM-CRF model is applied to Chinese electronic medical records to recognize related named entities in these records. For the characteristics of Chinese electronic medical records, firstly, the one-hot vector of each word is obtained in units of sentences. Secondly, map one-hot vector to a low-dimensional dense word vector. Thirdly, word vector is used as the input of the BiLSTM layer to achieve automatic extraction of sentence features. Finally, the CRF layer performs sequence-level labeling of sentences. In addition, drug dictionary and post-correction rules are added to correct the segmentation error of entity boundary, to improve recognition accuracy of related named entities. The F1 value of this method on a given test data set is 87.68%.