NAMED ENTITY RECOGNITION BASED ON MULTI-HEAD ATTENTION IN CHINESE ELECTRONIC MEDICAL RECORDS

Aimed at the recognition problem of complex medical entities in Chinese electronic medical records (EMRs), an entity recognition method combining joint features and multi-head attention is proposed. This method used the joint feature composed of characters, parts of speech and dictionary, and used BiLSTM and multi-head attention to extract separately the global feature and local feature of the sentence. CRF was used to combine all the features to complete the prediction of the entity labels. Experimental results show that the F1-score of this method reaches 89.16%, among which the two types of entities, treatment and disease, reach 94.76% and 95.56% respectively.