Abstract
As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification.
Highlights
Traditional Chinese medicine has been used for treatment and prevention of diseases and healthcare for thousands of years in China
Based on k Nearest Neighbor (k-NN) classifier, the proposed method achieved the best performance compared to document frequency (DF), information gain (IG), and mutual information (MI) feature selection methods
After the survey from a machine learning perspective, we find out several situations and issues of current objective researches on patient classification for traditional Chinese medicine (TCM), which are summarized as follows: (1) For the four diagnostic methods, a large amount of works focuses on the inspection and palpation by using various machine learning algorithms
Summary
From the perspective of TCM practitioners, both syndromes and diseases should be diagnosed upon the patients’ symptoms and signs This is due to both syndromes and diseases can providing information for making a prescription on the Chinese medical treatment. Computational methods for TCM have been developed to allow experts to identify and diagnose pathological information and explore these potential relationships which are unknown between current TCM and western medicine For these methods, the ultimate purpose is to classify patients with different diseases or syndromes and investigate their potential relationships. Patient classification is critical for clinical uniformity and efficacy diagnosed by different TCM experts, and for the development of TCM objectification which could consummate the understanding of the relationships among different syndrome types, symptoms/signs, and diseases.
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