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| Medical Named Entity Recognition with Domain Knowledge |
Pei Wei1,2,3,Sun Shuifa1,2,3,Li Xiaolong2,4,Lu Ji2,Yang Liu5,Wu Yirong6( ) |
1Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydropower Engineering, China Three Gorges University, Yichang 443002, China
2Yichang Key Laboratory of Intelligent Medicine, Yichang 443002, China
3College of Computer and Information Technology, China Three Gorges University,Yichang 443002, China
4College of Economics & Management,China Three Gorges University,Yichang 443002, China
5Faculty of Psychology,Beijing Normal University, Zhuhai 519087, China
6Institute of Advanced Studies in Humanities and Social Sciences, Beijing Normal University,Zhuhai 519087, China |
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Abstract [Objective] This paper builds a graph neural network model integrating medical domain knowledge(GraphModel-Dict) to identify named entities from medical texts. [Methods] First, we used the graph neural network structure to integrate domain knowledge, mapping the raw text data and domain dictionaries as nodes of different categories. We also updated the nodes of raw text data with Gated Recurrent Unit (GRU) to obtain their semantic representation with domain knowledge. Then, we used the representation of the text data node as an input to a Bidirectional Long Short-Term Memory network (BiLSTM). We predicted the labels and generated recognition results with a Conditional Random Field (CRF) model. Finally, we evaluated GraphModel-Dict’s performance on two datasets. [Results] We examined the GraphModel-Dict on a manually annotated dataset of 3,100 Chinese ultrasound examination reports on breast cancer. The model’s precision, recall, and F1-score for entity recognition reached 96.91%, 97.52%, and 97.22%, respectively. Furthermore, GraphModel-Dict showed better recognition performance for entity types with fewer sample data or diverse expressions. On the CCKS2020 medical dataset, the F1-value of GraphModel-Dict increased by at least 1.39% compared to the baseline model. [Limitations] More research is needed to examine the effectiveness of the proposed model in other fields. [Conclusions] Integrating domain knowledge can improve the effectiveness of named entity recognition, which benefits medical information mining and clinical research.
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Received: 15 April 2022
Published: 09 November 2022
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| Fund:National Social Science Fund Project(20BTQ066) |
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Corresponding Authors:
Wu Yirong，ORCID:0000-0003-3535-2033，E-mail:yirongwu@hotmail.com。
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