Abstract
The electronic medical record (EMR) is a patient’s individual medical record written by health care providers to describe the medical activities of patients. Named entity recognition (NER) of EMR is helpful to extract important information from a large number of unstructured texts, which lays a foundation for medical data mining and application. The named entity of Traditional Chinese Medicine (TCM) is more complex and its length is uncertain. In order to explore the effective method of named entity recognition of TCM EMR, after comparing the existing entity recognition methods and models, this paper selects three models, BiLSTM-CRF, lattice LSTM-CRF and BERT, to recognize the symptom entities in EMR, and carries out comparative experiments. After the real EMR data was manually labeled, three models were used to train, and the precision, recall and F1 value were used to evaluate the recognition effect of the model. The experimental results show that BERT model has the best recognition effect about TCM EMR, the precision is 89.94%, the recall is 88.27%, and the F1 value is 89.10%.
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Acknowledgements
This study was supported by the Health Commission of Hubei Province Guiding Project (#WJ2019F185).
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Lin, F., Xie, D. (2020). Research on Named Entity Recognition of Traditional Chinese Medicine Electronic Medical Records. In: Huang, Z., Siuly, S., Wang, H., Zhou, R., Zhang, Y. (eds) Health Information Science. HIS 2020. Lecture Notes in Computer Science(), vol 12435. Springer, Cham. https://doi.org/10.1007/978-3-030-61951-0_6
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DOI: https://doi.org/10.1007/978-3-030-61951-0_6
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