Abstract
Traditional Chinese Medicine prescriptions are regarded as an important resource that brings together the treatment experience and wisdom of doctors of all ages. Effectively sorting out and excavating them, especially combining with their functional indications and medication rules, can improve clinical efficacy and new drug research and development. Because the classification system of prescriptions is not completely unified, the data of Chinese patent medicines, national standard formulae and the seventh editions of Chinese formula textbooks are manually integrated, and a quadrat data set containing 21 efficacy classifications is constructed. Since on the text description of prescription information (prescription name, composition, indications and efficacy) in the data set, a variety of deep learning text classification models are used to automatically judge prescription classification, so as to establish an efficient and accurate classification model, and finally construct a prescription efficacy classification data set. The experimental results show that the pre-trained Bert-CNN model has the best effect, with the accuracy rate of 77.87%, and the weighted accuracy rate, weighted recall rate and weighted F1 value of 79.46%, 77.87% and 77.44%, respectively. This study provides a useful reference for further realizing the automatic information processing of ancient formulas.
Supported by the National Natural Science Foundation of China (82174534, 61772249), and the Fundamental Research Funds for the Central Public Welfare Research Institutes (ZZ160311).
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This work is supported by the National Natural Science Foundation of China (82174534, 61772249), and the Fundamental Research Funds for the Central Public Welfare Research Institutes (ZZ160311).
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Ren, Q., Li, K., Yang, D., Zhu, Y., Yao, K., Meng, X. (2023). TCM Function Multi-classification Approach Using Deep Learning Models. In: Yuan, L., Yang, S., Li, R., Kanoulas, E., Zhao, X. (eds) Web Information Systems and Applications. WISA 2023. Lecture Notes in Computer Science, vol 14094. Springer, Singapore. https://doi.org/10.1007/978-981-99-6222-8_21
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