Abstract: To solve the problem that traditional methods are too dependent on dictionaries and word segmentation tools in entity extraction of Chinese Electronic Medical Records and cannot make full use of contextual features, this paper proposes a Chinese EMR entity extraction model based on the combination of word embedded convolution (CNN), bidirectional gated loop unit (BGRU) and conditional random field (CRF). In the first place, the word embedding method is used to extract the potential word features, and then the attention mechanism is used to highlight the specific information while using the joint method of word features. At last, the final result is obtained by rationality constraint. This model makes full use of word features to avoid the influence of wrong word segmentation on entity extraction and to reduce the process of artificial feature construction, improve the efficiency of entity extraction. The experimental results show that the F value of the model performs better than the traditional Bi-LSTM-CRF model in entity extraction of diagnosis name, symptom name and treatment type. 

Key words: Chinese electronic medical record, entity extraction, CNN, BGRU, attention mechanism