BUILDING THE SUPPORTED MODEL FOR PREDICTING AND TREATING LIVER DISEASES IN TRADITIONAL VIETNAMSES MEDICINE USING MACHINE LEARNING

Minh Đức Phan 1,, Thị Trang Nguyễn 1, Thị Mai Vũ 1, Phương Ngân Nguyễn1, Thị Bích Hà Tạ 1, Đình Khiết Lê
1 VNU Hanoi-University of Medicine and Pharmacy

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Abstract

Recently, the development of traditional Vietnames medicine has become urgent to meet expectations from practical needs. However, this issue is facing many challenges when approached by methods according to classical paths. In that context, data – driven approach is expected to accelerate the development process. Following this direction, this study has been conducted to build models to support diagnosis and treatment of liver diseases in traditional medicine using machine learning methods. Experiments on a dataset collected from common traditional medical documents, the model has provided high compatibility predictions (100%) and proposed further diagnosis steps. In addition, methodology has been applied to screen 46 (out of more than 62500) relationships between symptoms and herbal remedies. The significance of these relationships has been noted by comparison with the pharmacopeia. The initial results of the study show the potential for clinical support applications, beyond contributing to the promotion of medical informatics.

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References

1. Zhou, Z., Chen, B., Chen, S., Lin, M., Chen, Y., Jin, S., & Zhang, Y. (2020). Applications of network pharmacology in traditional Chinese medicine research. Evidence-Based Complementary and Alternative Medicine, 2020.
2. Chen, M., Jiang, Y., Cao, Y., & Zomaya, A. Y. (2020). CreativeBioMan: a brain-and body-wearable, computing-based, creative gaming system. IEEE Systems, Man, and Cybernetics Magazine, 6(1), 14-22.
3. He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019). The practical implementation of artificial intelligence technologies in medicine. Nature medicine, 25(1), 30-36.
4. Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., & Tekade, R. K. (2021). Artificial intelligence in drug discovery and development. Drug discovery today, 26(1), 80.
5. Biên, N. T. (2020). Điều tra, sưu tầm, tổng hợp nguồn thực vật, động vật, khoáng vật làm thuốc tại tỉnh Lâm Đồng để xây dựng danh lục tài nguyên dược liệu tỉnh Lâm Đồng.
6. Cù, K. L., Trần, M. T., Lê, H. S., Lương, T. H. L., Phạm, M. C., Nguyễn, T. T., & Phạm, V. H. (2021). Chẩn đoán bệnh trong y học cổ truyền: Hướng tiếp cận dựa trên đồ thị tri thức mờ dạng cặp. Các công trình nghiên cứu, phát triển và ứng dụng Công nghệ Thông tin và Truyền thông, 59-68.
7. Bretthorst, G. L. (1990). An introduction to parameter estimation using Bayesian probability theory. In Maximum Entropy and Bayesian Methods (pp. 53-79). Dordrecht: Springer Netherlands.
8. Abdel-Basset, M., Mohamed, M., Smarandache, F., & Chang, V. (2018). Neutrosophic association rule mining algorithm for big data analysis. Symmetry, 10(4), 106.