Chinese Electronic Medical Record Named Entity Recognition Based on ChineseBERT Model
CHEN Xuesong
ZHOU Dongdong
WANG Haochang
Abstract：Extracting valuable medical information from Chinese electronic medical records has become a popular research topic.The combination of BERT and neural networks has become the mainstream in the field of named entity recognition.Previous Chinese pre-training models have ignored two important features of Chinese characters,which are glyph and pinyin,they contain important grammatical and semantic information in language understanding.Therefore,ChineseBERT pre-training model is used,it integrates Chinese glyph and pinyin information into the model pre-training,and inputs the obtained word vectors into bidirection-al long short-term memory Network(BiLSTM)to obtain contextual features after adversarial training,and finally inputs conditional random field(CRF)decoding to get the final prediction result.The experimental results on the CCKS2019 dataset show that Chine-seBERT-BiLSTM-CRF model gets a F1 value of 84.96%,which can be applied to the task of Chinese electronic medical record named entity recognition.
Keywords：named entity recognitionelectronic medical recordsadversarial trainingChineseBERTBiLSTMCRF
Publication Date：2025-11-20
Online Publishing Date：2026-01-28(First online date of this platform, not the publication date of the document)
Pages：6( 3139-3143,3154 )