计算机与数字工程2025，Vol.53Issue(11)：3139-3143,3154,6.DOI:10.3969/j.issn.1672-9722.2025.11.026
基于ChineseBERT模型的中文电子病历命名实体识别
Chinese Electronic Medical Record Named Entity Recognition Based on ChineseBERT Model
摘要
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.关键词
命名实体识别/电子病历/对抗训练/ChineseBERT/BiLSTM/CRFKey words
named entity recognition/electronic medical records/adversarial training/ChineseBERT/BiLSTM/CRF分类
信息技术与安全科学引用本文复制引用
陈雪松,周冬冬,王浩畅..基于ChineseBERT模型的中文电子病历命名实体识别[J].计算机与数字工程,2025,53(11):3139-3143,3154,6.基金项目
国家自然科学基金项目(编号:61402099,61702093)资助. （编号:61402099,61702093）
