Abstract
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.
Highlights
As an important complementary medical system to modern biomedicine, traditional Chinese medicine (TCM) has played an indispensable role in healthcare of Chinese people for several thousand years [1, 2]
We evaluate our framework, syndrome differentiation topic model (SDTM), on three experimental tasks for Chinese medical records
We want to determine the following: (i) Can our SDTM achieve the best generalization performance compared to other topic models?
Summary
As an important complementary medical system to modern biomedicine, traditional Chinese medicine (TCM) has played an indispensable role in healthcare of Chinese people for several thousand years [1, 2]. The TCM has become more and more popular all over the world [3]. Doctors usually adopt four diagnostic ways to obtain symptoms, that is, observation, listening, interrogation, and pulse-taking in TCM [4]. A syndrome can be summarized via a set of symptoms, which are intrinsically related to each other. An example of syndrome is given, which is selected from [4]. It includes syndrome name, symptoms, pathogenesis, treatment, representative prescription, and common medicines [5,6,7]
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