Medical named entity recognition based on data augmentation and structural feature fusion

In response to the issues of limited annotated data and insufficient semantic information extraction in medical named entity recognition for Chinese electronic medical records, a novel approach based on data augmentation and structural feature fusion is proposed. Firstly, addressing the potential noise introduced by unsupervised data augmentation methods like synonym replacement and random insertion, a rule-based data augmentation technique is introduced. This method effectively diversifies the annotated Chinese electronic medical record data by making use of external resources. Considering that Chinese characters are ideograms and characters with similar radical structures often convey similar semantic information, a semantic information enhancer is proposed. This enhancer utilizes a convolutional neural network to obtain vector representations of Chinese character features. Based on cosine similarity, it extracts several characters with the highest radical structure similarity, and incorporates additional semantic information through an attention-weighted fusion mechanism. This enables the model to extract semantic information more effectively. Lastly, a semantic fusion module is constructed to integrate the semantic information enhancer into the primary named entity recognition model, employing a Conditional Random Field (CRF) to predict the final results. Experimental results on the CCKS 2019 Chinese electronic medical record dataset demonstrate that the model achieves an impressive F1 score of 83.71%, validating the effectiveness of this approach.