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Electronic Medical Record Entity Recognition via Machine Reading Comprehension and Biaffine

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  • Jun Cao
  • Xian Zhou
  • Wangping Xiong
  • Ming Yang
  • Jianqiang Du
  • Yanyun Yang
  • Tianci Li
  • Waqas Haider Bangyal

Abstract

The entity recognition of Chinese electronic medical record is of great significance to medical decision-making. The main process of entity recognition is sequence tagging, which has problems such as nested entity and boundary prediction. In this paper, we proposed a NER method called Bert-MRC-Biaffine, which formulates the NER as an MRC task. The approach of the machine reading comprehension framework is to introduce prior knowledge, the query about entities. The biaffine mechanism scores pair start and end tokens in a sentence so that the model is able to predict named entities accurately. The proposed method outperforms from the electronic medical record dataset, called CCKS2017 data, and the TCM dataset. We also remove components to evaluate the contribution of individual components of our model. Experiments on two datasets demonstrate the effectiveness of our model.

Suggested Citation

  • Jun Cao & Xian Zhou & Wangping Xiong & Ming Yang & Jianqiang Du & Yanyun Yang & Tianci Li & Waqas Haider Bangyal, 2021. "Electronic Medical Record Entity Recognition via Machine Reading Comprehension and Biaffine," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-8, October.
    • Handle: RePEc:hin:jnddns:1640837
    DOI: 10.1155/2021/1640837
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