Electronic Medical Record Entity Recognition via Machine Reading Comprehension and Biaffine. (19th October 2021)

• Record Type: Journal Article
• Authors: Cao, Jun; Zhou, Xian; Xiong, Wangping; Yang, Ming; Du, Jianqiang; Yang, Yanyun; Li, Tianci
• Other Names: Bangyal Waqas Haider Academic Editor.
• Is Part Of: Discrete dynamics in nature and society. Volume 2021(2021)
• Publication Date: 2021-10-19
• Subjects: System analysis -- Periodicals; Dynamics -- Periodicals; Chaotic behavior in systems -- Periodicals; Differentiable dynamical systems -- Periodicals; 003.05
• DOI: 10.1155/2021/1640837
• Languages: English
• ISSNs: 1026-0226

Abstract

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.