Study on Medical Entity Recognition in Chinese academic articles on Hypertension

The research results of Chinese Medical academic articles are more accurate and representative than other medical texts. This paper studies the extraction method of medical entity recognition in Chinese academic articles on hypertension. This paper proposes a BiLSTM-CRF (Bidirectional Long Short-Term Memory and Conditional Random Fields) model blended with TFE (Text Feature Embedding) and BERT (Bidirectional Encoder Representation from Transformers) for Chinese medical entity recognition. BERT model was used for text pre-processing while text features of Chinese medical academic articles were obtained, and then the annotation and classification of medical entity were completed via three models. Compared with the BiLSTM-CRF model and BERT-BiLSTM-CRF model, the recognition effect of the BERT-TFE-BiLSTM-CRF model proposed in this study was better than the other two models and the F1 value of model was 6.9% and 3.3% higher respectively. The BERT-TFE-BiLSTM-CRF model can effectively recognize medical entity in Chinese academic articles on hypertension.