Named Entity Recognition of Vascular Surgery Based on Glyph Features
As core components of healthcare information systems,Electronic Medical Record(EMR)entails numerous important medical entities.Named Entity Recognition(NER)of EMRs can significantly advance medical research.To address the challenges of limited research data and complex entity recognition in vascular surgery EMRs,a small-scale specialized dataset is constructed using real clinical data obtained from the vascular surgery department of a tertiary hospital.A NER model based on glyph features is proposed to improve the recognition accuracy.First,dynamic character vectors are generated using the Masked Language Model(MLM)as correction Bidirectional Encoder Representations from Transformers(MacBERT)and incorporating glyph information via the Chinese four-corner code and Wubi input methods.The text representations are then fed into a Bi-directional Gated Recurrent Unit(BiGRU)and Gated Dilated Convolutional Neural Network(DGCNN)for feature extraction,and the outputs are subsequently concatenated.Finally,the model employs a multihead self-attention mechanism to capture the relationships between sequence elements and uses Conditional Random Field(CRF)for label decoding.Experimental results demonstrate that the proposed model achieves precision,recall,and F1 scores of 96.45％,97.77％,and 97.10％,respectively,on the self-constructed vascular surgery dataset.These results indicate that the proposed model outperforms the comparison models and demonstrates superior entity recognition performance.
Electronic Medical Record(EMR)vascular surgeryNamed Entity Recognition(NER)feature fusiondeep learning
万方数据
维普
