Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective
Joint Authors
Wang, Chengjun
Niu, Jinling
Zhao, Changbo
Li, Guo-zheng
Source
Evidence-Based Complementary and Alternative Medicine
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-07-12
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Abstract EN
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 signsexamined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes ofdisease 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 onpatient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification.
With the consideration of different diagnosticdata 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.
American Psychological Association (APA)
Zhao, Changbo& Li, Guo-zheng& Wang, Chengjun& Niu, Jinling. 2015. Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective. Evidence-Based Complementary and Alternative Medicine،Vol. 2015, no. 2015, pp.1-18.
https://search.emarefa.net/detail/BIM-1061425
Modern Language Association (MLA)
Zhao, Changbo…[et al.]. Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective. Evidence-Based Complementary and Alternative Medicine No. 2015 (2015), pp.1-18.
https://search.emarefa.net/detail/BIM-1061425
American Medical Association (AMA)
Zhao, Changbo& Li, Guo-zheng& Wang, Chengjun& Niu, Jinling. Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective. Evidence-Based Complementary and Alternative Medicine. 2015. Vol. 2015, no. 2015, pp.1-18.
https://search.emarefa.net/detail/BIM-1061425
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1061425