Mining Syndrome Differentiating Principles from Traditional Chinese Medicine Clinical Data

作     者：Jialin Ma Zhaojun Wang Hai Guo Qian Xie Tao Wang Bolun Chen 

作者机构：Jiangsu Internet of Things and Mobile Internet Technology Engineering LaboratoryHuaiyin Institute of TechnologyHuaian223003China Huaiyin Wu Jutong Institute of Traditional Chinese MedicineHuaian223000China The Affiliated Huaian No.1 People’s Hospital of Nanjing Medical UniversityHuaian223000China Jiangsu Eazytec Co.Ltd.WuxiChina University of FribourgFribourg1700Switzerland 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2022年第40卷第3期

页      面：979-993页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：TCM syndrome differentiation topic model LDA SSTM 

摘      要：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.