模糊性
潜在Dirichlet分配
计算机科学
相似性(几何)
过程(计算)
主题模型
中医药
人工智能
语义学(计算机科学)
自然语言处理
数据科学
数据挖掘
机器学习
情报检索
医学
病理
替代医学
图像(数学)
程序设计语言
操作系统
模糊逻辑
作者
Jialin Ma,Xiaoqiang Gong,Zhaojun Wang,Qian Xie
摘要
Syndrome differentiation is the most basic diagnostic method in traditional Chinese medicine (TCM). The process of syndrome differentiation is difficult and challenging due to its complexity, diversity, and vagueness. Recently, artificial intelligent methods have been introduced to discover the regularities of syndrome differentiation from TCM medical records, but the existing DM algorithms failed to consider how a syndrome is generated according to TCM theories. In this paper, we propose a novel topic model framework named syndrome differentiation topic model (SDTM) to dynamically characterize the process of syndrome differentiation. The SDTM framework utilizes latent Dirichlet allocation (LDA) to discover the latent semantic relationship between symptoms and syndromes in mass of Chinese medical records. We also use similarity measurement method to make the uninterpretable topics correspond with the labeled syndromes. Finally, Bayesian method is used in the final differentiated syndromes. Experimental results show the superiority of SDTM over existing topic models for the task of syndrome differentiation.
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