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

Ruichong Huang; Liang Li

doi:10.1117/12.3029713

21 June 2024 Medical named entity recognition based on data augmentation and structural feature fusion

Ruichong Huang, Liang Li

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131673I (2024) https://doi.org/10.1117/12.3029713
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ruichong Huang and Liang Li "Medical named entity recognition based on data augmentation and structural feature fusion", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131673I (21 June 2024); https://doi.org/10.1117/12.3029713

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KEYWORDS

Semantics

Data modeling

Feature extraction

Transformers

Education and training

Anatomy

Performance modeling

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