Abstract
One of the major research contents carried by scholars of Traditional Chinese medical science (TCM) is to discover the compatibility rules of herbs to increase the efficacy in treating certain syndromes. However, up to now, most of the compatibility rules of herbs are based on empirical analyses, which make them hard to study. Since concepts of Big Data and machine learning have been popularized gradually, how to use data mining techniques to effectively figure out core herbs and compatibility rules becomes the main research aspect of TCM informatics. In this paper, the hypergraph partitioning algorithm HMETIS based on Apriori is applied to exploit and analyze clinical data about lung cancer. The result shows that all 15 Chinese herbs obtained by the algorithm accord with the core concepts of the treatment of lung cancer by experienced TCM doctors, namely replenishing nutrition, clearing heat-toxin, resolving phlegm and eliminating pathogenic factors.
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Acknowledgment
This work was supported by National Natural Science Foundation of China (Grant No. 61301028); Natural Science Foundation of Shanghai China (Grant No. 13ZR1402900); Doctoral Fund of Ministry of Education of China (Grant No. 20120071120016).
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Wang, M. et al. (2016). The Study of the Compatibility Rules of Traditional Chinese Medicine Based on Apriori and HMETIS Hypergraph Partitioning Algorithm. In: Wang, F., Luo, G., Weng, C., Khan, A., Mitra, P., Yu, C. (eds) Biomedical Data Management and Graph Online Querying. Big-O(Q) DMAH 2015 2015. Lecture Notes in Computer Science(), vol 9579. Springer, Cham. https://doi.org/10.1007/978-3-319-41576-5_2
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