针对中文电子病历中医疗嵌套实体难以处理的问题, 本文基于RoBERTa-wwm-ext-large预训练模型提出一种知识增强的中文电子病历命名实体识别模型ERBAEGP. RoBERTa-wwm-ext-large采用的全词掩码策略能够获得词级别的语义表示, 更适用于中文文本. 首先结合知识图谱, 使模型学习到了大量的医疗实体名词, 进一步提高模型对电子病历实体识别的准确性. 然后通过BiLSTM对电子病历输入序列编码, 能够更好捕获病历的中上下语义信息. 最后利用全局指针网络模型EGP (efficient GlobalPointer)同时考虑实体的头部和尾部的特征信息来预测嵌套实体, 更加有效地解决中文电子病历命名实体识别任务中嵌套实体难以处理的问题. 在CBLUE中的4个数据集上本文方法均取得了更好的识别效果, 证明了ERBAEGP模型的有效性.
Regarding the challenge of handling nested medical entities in Chinese electronic medical records, this study proposes a knowledge-enhanced named entity recognition model for Chinese electronic medical records called ERBAEGP based on the RoBERTa-wwm-ext-large pre-trained model. The comprehensive word masking strategy employed by the RoBERTa-wwm-ext-large model can obtain semantic representations at the word level, which is more suitable for Chinese texts. First, the model learns a significant number of medical entity nouns by integrating knowledge graphs, further improving entity recognition accuracy in electronic medical records. Then, the contextual semantic information within the records can be better captured through BiLSTM encoding of the input sequence of medical records. Finally, the efficient GlobalPointer (EGP) model is adopted to simultaneously consider the features of both the head and tail of entities to predict nested entities, addressing the challenge of handling nested entities in named entity recognition tasks of Chinese electronic medical records. The effectiveness of the ERBAEGP model is demonstrated by yielding better recognition results on the four datasets within CBLUE.