Chinese Clinical Named Entity Recognition Using Multi-Feature Fusion and Multi-Scale Local Context Enhancement

Chinese Clinical Named Entity Recognition (CNER) is a crucial step in extracting medical information and is of great significance in promoting medical informatization. However, CNER poses challenges due to the specificity of clinical terminology, the complexity of Chinese text semantics, and the uncertainty of Chinese entity boundaries. To address these issues, we propose an improved CNER model, which is based on multi-feature fusion and multi-scale local context enhancement. The model simultaneously fuses multi-feature representations of pinyin, radical, Part of Speech (POS), word boundary with BERT deep contextual representations to enhance the semantic representation of text for more effective entity recognition. Furthermore, to address the model’s limitation of focusing just on global features, we incorporate Convolutional Neural Networks (CNNs) with various kernel sizes to capture multi-scale local features of the text and enhance the model’s comprehension of the text. Finally, we integrate the obtained global and local features, and employ multi-head attention mechanism (MHA) extraction to enhance the model’s focus on characters associated with medical entities, hence boosting the model’s performance. We obtained 92.74%, and 87.80% F1 scores on the two CNER benchmark datasets, CCKS2017 and CCKS2019, respectively. The results demonstrate that our model outperforms the latest models in CNER, showcasing its outstanding overall performance. It can be seen that the CNER model proposed in this study has an important application value in constructing clinical medical knowledge graph and intelligent Q&A system.