Research on Named Entity Recognition of Electronic Medical Records Based on RoBERTa and Radical-Level Feature

Clinical named entity recognition (CNER) identifies entities from unstructured medical records and classifies them into predefined categories. It is of great significance for follow-up clinical studies. Most of the existing CNER methods fail to give enough thought to Chinese radical-level characteristics and the specialty of the Chinese field. This paper proposes the Ra-RC model, which combines radical features and a deep learning structure to fix this problem. A bidirectional encoder representation of transformer (RoBERTa) is utilized to learn medical features thoroughly. Simultaneously, we use the bidirectional long short-term memory (BiLSTM) network to extract radical-level information to capture the internal relevance of characteristics and stitch the eigenvectors generated by RoBERTa. In addition, the relationship between labels is considered to obtain the optimal tag sequence by applying conditional random field (CRF). The experimental results demonstrate that the proposed Ra-RC model achieves F1 score 93.26% and 82.87% on the CCKS2017 and CCKS2019 datasets, respectively.