Chinese electronic health record named entity recognition algorithm BLF-MarkBERT
With the development of deep learning technology,Chinese named entity recognition has made significant progress in various fields,especially in the field of Chinese electronic medical records,where it has become an important task in the field of medical information management.Chinese electronic medical record named entity recognition automatically identifies and classi-fies named entities from electronic medical records,improves medical information management efficiency and clinical decision sup-port,and promotes the development of medical intelligent informatization.In order to further improve its effect,this paper improves and implements a deep learning model BLF-MarkBERT based on the MarkBERT method that integrates the bidirectional long short-term memory network and decoding method.Experimental results on the CCKS2019 data set show that BLF-MarkBERT is better than the comparison algorithm in the three evaluation indicators of Precision(P),Recall(R)and F1-score,indicating the su-periority of the model.
Chinese named entity recognitionMarkBERTBiLSTMChinese electronic medical record
NETL
NSTL
万方数据
