Improving the Named Entity Recognition of Chinese Electronic Medical Records by Combining Domain Dictionary and Rules

Electronic medical records are an integral part of medical texts. Entity recognition of electronic medical records has triggered many studies that propose many entity extraction methods. In this paper, an entity extraction model is proposed to extract entities from Chinese Electronic Medical Records...

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Bibliographic Details
Published in:International journal of environmental research and public health 2020-04, Vol.17 (8), p.2687
Main Authors: Chen, Xianglong, Ouyang, Chunping, Liu, Yongbin, Bu, Yi
Format: Article
Language:English
Subjects:
Conditional random fields
Datasets
Deep learning
Dictionaries
Disease
Domains
Electronic health records
Electronic medical records
Long short-term memory
Lymphatic system
Machine learning
Medical records
Medical research
Natural language processing
Neural networks
Recognition
Representations
Researchers
Semantics
Support vector machines
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Summary:Electronic medical records are an integral part of medical texts. Entity recognition of electronic medical records has triggered many studies that propose many entity extraction methods. In this paper, an entity extraction model is proposed to extract entities from Chinese Electronic Medical Records (CEMR). In the input layer of the model, we use word embedding and dictionary features embedding as input vectors, where word embedding consists of a character representation and a word representation. Then, the input vectors are fed to the bidirectional long short-term memory to capture contextual features. Finally, a conditional random field is employed to capture dependencies between neighboring tags. We performed experiments on body classification task, and the F1 values reached 90.65%. We also performed experiments on anatomic region recognition task, and the F1 values reached 93.89%. On both tasks, our model had higher performance than state-of-the-art models, such as Bi-LSTM-CRF, Bi-LSTM-Attention, and Vote. Through experiments, our model has a good effect when dealing with small frequency entities and unknown entities; with a small training dataset, our method showed 2-4% improvement on F1 value compared to the basic Bi-LSTM-CRF models. Additionally, on anatomic region recognition task, besides using our proposed entity extraction model, 12 rules we designed and domain dictionary were adopted. Then, in this task, the weighted F1 value of the three specific entities extraction reached 84.36%.
ISSN:1660-4601
1661-7827
1660-4601
DOI:10.3390/ijerph17082687