Research on Entity Recognition of Chinese Obstetrics and Gynecology Electronic Medical Records Based on K-BERT
When the pre-trained model is used to name entity recognition of Chinese obstetrics and gynecology electronic medical records,BERT lacks certain professional knowledge in the medical field,which leads to the decline of its recognition performance.A pre-trained model based on knowledge graph-K-BERT name entity recognition model K-BERT-BiLSTM-CRF is proposed.The K-BERT pre-training model is used to obtain the semantic feature vector containing the medical background knowledge,and the bidirectional long short-term memory network(BiLSTM)and conditional random field(CRF)are used to extract the context-related features and solve the label offset problem to complete the entity recognition.Using the real obstetrics and gynecology medical electronic medical record data set for training,the F1 value of the K-BERT-BiLSTM-CRF model reached 90.04％.Experiments show that compared with the general BERT model,the K-BERT-BiLSTM-CRF name entity recognition model performs better in the field of Chinese obstetrics and gynecology electronic medical records,and the recognition effect is better.
K-BERTBidirectional long short-term memoryConditional random fieldsObstetrics and gynecology electronic medical recordsName entity recognition
国家科技期刊平台
NSTL
万方数据
维普
