Named Entity Recognition in Chinese Electronic Medical Records Using Knowledge Graph Construction

Corresponding author: zhangfeng, trees_357@126.com

DOI: 10.12201/bmr.202312.00011

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  Abstract: Abstract Objective/Meaning: To explore the technical feasibility of named entity recognition method based on Chinese electronic medical records in the construction of medical knowledge map and related application promotion. Methods/Process: Using the large-scale real-world medical electronic medical record data to fine-tune the word embedding representation model RoBERTa to build the proprietary embedded representations of the medical terms. Leveraging convolutional neural network model to extract local semantic features. Finally, a stacked BiLSTM is constructed, which has a multi-layer structure and a novel stacked method. Results/Conclusions: The stacked attention network model proposed in this paper achieves 91.5% on F1 value, which has a stronger medical named entity recognition performance than other advanced models. The stacked attention network is proposed to further solve the task of Chinese medical named entity recognition, which can achieve comprehensive and in-depth extraction of global semantic features and reduce the time cost.

  Key words: Electronic Medical Record; Knowledge Graph; Named Entity Recognition; Stacked Attention Network; Bidirectional Encoder Representation from Transformers

  Submit time: 11 December 2023

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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• ID	Submit time	Number	Download
  1	2023-04-06	bmr.202312.00011V1	Download

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