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Mining Syndrome Differentiating Principles from Traditional Chinese Medicine Clinical Data

Jialin Ma1,*, Zhaojun Wang2, Hai Guo3, Qian Xie1,4, Tao Wang4, Bolun Chen5

1 Jiangsu Internet of Things and Mobile Internet Technology Engineering Laboratory, Huaiyin Institute of Technology, Huaian, 223003, China
2 Huaiyin Wu Jutong Institute of Traditional Chinese Medicine, Huaian, 223000, China
3 The Affiliated Huaian No.1 People’s Hospital of Nanjing Medical University, Huaian, 223000, China
4 Jiangsu Eazytec Co., Ltd., Wuxi, China
5 University of Fribourg, Fribourg, 1700, Switzerland

* Corresponding Author: Jialin Ma. Email: email

Computer Systems Science and Engineering 2022, 40(3), 979-993.


Syndrome differentiation-based treatment is one of the key characteristics of Traditional Chinese Medicine (TCM). The process of syndrome differentiation is difficult and challenging due to its complexity, diversity and vagueness. Analyzing syndrome principles from historical records of TCM using data mining (DM) technology has been of high interest in recent years. Nevertheless, in most relevant studies, existing DM algorithms have been simply developed for TCM mining, while the combination of TCM theories or its characteristics with DM algorithms has rarely been reported. This paper presents a novel Symptom-Syndrome Topic Model (SSTM), which is a supervised probabilistic topic model with three-tier Bayesian structure. In the SSTM, syndromes are considered as observed topic labels to distinguish certain symptoms from possible symptoms according to their different positions. The generation of our model is in full compliance with the syndrome differentiation theory of TCM. Experimental results show that the SSTM is more effective than other models for syndrome differentiating.


Cite This Article

J. Ma, Z. Wang, H. Guo, Q. Xie, T. Wang et al., "Mining syndrome differentiating principles from traditional chinese medicine clinical data," Computer Systems Science and Engineering, vol. 40, no.3, pp. 979–993, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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