Chinese medical named entity recognition integrating adversarial training and feature enhancement

Chinese possesses the essential attributes of unique character composition structure and the nested nature of medical entities, which causes many challenges for Chinese Electronic Health Records (EHRs) in medical named entity recognition tasks, such as scarce annotated data, strong tokenization ambiguity, and blurred entity boundaries. This increases the difficulty of extracting medical named entity categories. The paper proposes an effective Chinese clinical named entity recognition model that integrates BERT and adversarial enhancement in a dual channel architecture to address this issue. Firstly, the model integrates various advanced technologies, such as Bidirectional Long Short-Term Memory networks (BiLSTM), Iterative Deep Convolutional Neural Networks (IDCNN), and Conditional Random Fields (CRF), to improve the accuracy of named entity recognition. Secondly, the paper collected texts from medical record websites and utilized the YEDDA tool for professional annotation and processing of these texts, ultimately forming a more comprehensive target dataset. This process ensures that the model is exposed to representative Chinese clinical data during training, thereby improving recognition performance.Finally, experimental results indicate that the BPBIC model achieved a precision of 93.80%, a recall of 94.44%, and an F1 score of 94.12% on the augmented dataset CCKS2019 (CCKS2019+). Moreover, through knowledge graph analysis of medical entities extracted from single and multiple disease EHRs, the model assists doctors in achieving rapid and accurate diagnoses, thereby enhancing the efficiency of healthcare professionals.