title: Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective.
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.
authors.affiliation: Department of Control Science and Engineering, Tongji University, Shanghai 201804, China.
authors.forename: Changbo
authors.initials: C
authors.lastname: Zhao
authors.affiliation: Department of Control Science and Engineering, Tongji University, Shanghai 201804, China.
authors.forename: Guo-Zheng
authors.initials: GZ
authors.lastname: Li
authors.affiliation: Department of Control Science and Engineering, Tongji University, Shanghai 201804, China.
authors.forename: Chengjun
authors.initials: C
authors.lastname: Wang
authors.affiliation: Department of Control Science and Engineering, Tongji University, Shanghai 201804, China.
authors.forename: Jinling
authors.initials: J
authors.lastname: Niu