Open Access

ARTICLE

Mining Syndrome Differentiating Principles from Traditional Chinese Medicine Clinical Data

Jialin Ma^1,*, Zhaojun Wang², Hai Guo³, Qian Xie^1,4, 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:

Computer Systems Science and Engineering 2022, 40(3), 979-993. https://doi.org/10.32604/csse.2022.016759

Received 11 January 2021; Accepted 30 April 2021; Issue published 24 September 2021

Abstract

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.

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Keywords

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