Research on named entity recognition of Chinese medical records based on BERT-BiLSTM-CRF with Chinese radicals

Corresponding author: xiaoxiaoxia, amily_x@hnucm.edu.cn

DOI: 10.12201/bmr.202303.00004

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  Abstract: Abstract Purpose Research on the method of extracting medical terms from chinese medical records, realize the automatic structure of medical records, and provide structured data for knowledge discovery of medical records. Method This paper proposes a deep learning named entity recognition model based on BERT combining LSTM, CRF and radical features. This model embeds chinese radicals in BERT word vector, extracts entity features with BiLSTM, and uses CRF for sequence prediction. 400 medical cases with a total of more than 50000 words manually marked are divided into training set and test set according to the ratio of 3 to 1, the model is used to identify four types of named entities in chinese medical records: body, medicine, symptom, and disease. Result The F1 value of this model on the test set is 84.81%, which is superior to other models without embedded radicals, indicating that the model can more effectively identify named entities in chinese medical records and better structured medical records.

  Key words: Entity recognition; Radical features; BERT; BiLSTM; CRF

  Submit time: 22 March 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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  1	2022-10-11	bmr.202303.00004V1	Download

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