Abstract
Traditional Chinese Medicine (TCM) is the treasure of Chinese civilization and plays an indispensable role in China’s medical system, but the diagnosis of TCM relies heavily on doctors’ experience, which can affect the accuracy of diagnosis in practice. With the development of natural language processing technology, its mechanism can learn from a large amount of unstructured text to obtain a comprehensive and unified classification model. In this paper, we take chest impediment disease ( i.e. coronary heart disease in Western medicine) as an example and build a pre-training diagnostic model based on the BERT model for TCM texts to accomplish the text classification task for different types of chest impediment medical records. Its overall F1 value reached 0.851, which improved 0.096 compared with the model without TCM pre-training; it also explored the problem of long text truncation and stopwords removing of TCM cases, which improved 0.087 compared with no TCM stopwords removing. This paper introduces natural language processing into the TCM auxiliary diagnosis problem, in order to improve the informationization, standardization and intelligence of TCM in the new era.
D. Qin—Received his Ph.D. degree in Department of Computer Science and Technology, Tsinghua University, China in 2013. He has published more than 50 papers in refereed international conferences and journals. He is a professor in School of Artificial Intelligence, GuangXi Minzu University, Nanning, China. His current research interests focus on Natural Language Processing, Algorithm Design, etc.
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References
Standardization project group of clinical application guidelines of proprietary Chinese medicines for the treatment of prevailing diseases: clinical application guidelines of proprietary Chinese medicines for the treatment of coronary heart disease (2020). Chinese J. Integrative Med. 41(04), 391–417 (2021)
The writing committee of the report on cardiovascular health and diseases in China: summary of report on cardiovascular health and disease in China 2020. Chinese Circ. J. 37(06), 553–578 (2022)
Candong, L.: Traditional Chinese Diagnostics. China Press of Traditional Chinese Medice, Beijng, China, pp. 206–207 (2016)
Zongyou, W.: A review of research on text mining of electronic medical records. Comput. Res. Dev. 58(03), 513–527 (2021)
Boli, Z., Mianhua, W.: Chinese Internal Medicine. China Press of Traditional Chinese Medice, Beijng, China, pp. 93–100 (2017)
Peters, M.-M.: Deep contextualized word representations. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 2227–2237. New Orleans, Louisiana (2018)
Radford, A., et al.: Improving language understanding by generative pre-training. https://s3-us-west-2.amazonaws.com/openaiassets/research-covers/languageunsupervised/language understanding paper.pdf (2018)
Jacob, D.-L.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Minneapolis, Minnesota (2019)
Mike, L.-L.: BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 7871–7880. Online (2020)
Vaswani, A.-N.: Attention is all you need. In: 31st International Conference on Neural Information Processing Systems, pp. 6000–6010. Curran Associates Inc., Red Hook, NY, USA (2017)
Candong, L.: Traditional Chinese Diagnostics. China Press of Traditional Chinese Medice, Beijng, China, p. 4 (2016)
Boli, Z., Mianhua, W.: Chinese Internal Medicine. China Press of Traditional Chinese Medice, Beijng, China, pp. 289–285 (2017)
Boli, Z., Mianhua, W.: Chinese Internal Medicine. China Press of Traditional Chinese Medice, Beijng, China, pp. 87–93 (2017)
Acknowledgment
This work was supported by the Guangxi Science and Technology Base and Talent Project (No. 2022AC16002), Horizontal Scientific Research Project of Guangxi Minzu University (No. 2022450016000429).
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Chen, H., Qin, D., Zhang, X., Zhang, H., Liang, X. (2023). A Study on the Classification of Chinese Medicine Records Using BERT, Chest Impediment as an Example. In: Liu, F., Duan, N., Xu, Q., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2023. Lecture Notes in Computer Science(), vol 14304. Springer, Cham. https://doi.org/10.1007/978-3-031-44699-3_3
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