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• W1569103839 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." @default.
• W1569103839 created "2016-06-24" @default.
• W1569103839 creator A5043430714 @default.
• W1569103839 creator A5061497411 @default.
• W1569103839 creator A5089711319 @default.
• W1569103839 creator A5091807808 @default.
• W1569103839 date "2015-01-01" @default.
• W1569103839 modified "2023-10-17" @default.
• W1569103839 title "Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective" @default.
• W1569103839 cites W1465734221 @default.
• W1569103839 cites W1484957654 @default.
• W1569103839 cites W1496415453 @default.
• W1569103839 cites W1499604918 @default.
• W1569103839 cites W1504113064 @default.
• W1569103839 cites W1527636277 @default.
• W1569103839 cites W1559838132 @default.
• W1569103839 cites W1589354338 @default.
• W1569103839 cites W1604528119 @default.
• W1569103839 cites W1827979610 @default.
• W1569103839 cites W1965097083 @default.
• W1569103839 cites W1976206027 @default.
• W1569103839 cites W1977064958 @default.
• W1569103839 cites W1978663284 @default.
• W1569103839 cites W1979240885 @default.
• W1569103839 cites W1979998635 @default.
• W1569103839 cites W1980928891 @default.
• W1569103839 cites W1981282317 @default.
• W1569103839 cites W1981571570 @default.
• W1569103839 cites W1984495457 @default.
• W1569103839 cites W1984756734 @default.
• W1569103839 cites W1986908923 @default.
• W1569103839 cites W1998533429 @default.
• W1569103839 cites W2002633700 @default.
• W1569103839 cites W2008499150 @default.
• W1569103839 cites W2010411374 @default.
• W1569103839 cites W2010751412 @default.
• W1569103839 cites W2013324130 @default.
• W1569103839 cites W2016187822 @default.
• W1569103839 cites W2025641306 @default.
• W1569103839 cites W2029657028 @default.
• W1569103839 cites W2030104143 @default.
• W1569103839 cites W2030206951 @default.
• W1569103839 cites W2036455951 @default.
• W1569103839 cites W2038690050 @default.
• W1569103839 cites W2038977683 @default.
• W1569103839 cites W2039942891 @default.
• W1569103839 cites W2047738340 @default.
• W1569103839 cites W2049332898 @default.
• W1569103839 cites W2052426165 @default.
• W1569103839 cites W2057716533 @default.
• W1569103839 cites W2060257057 @default.
• W1569103839 cites W2062930337 @default.
• W1569103839 cites W2063821826 @default.
• W1569103839 cites W2063894808 @default.
• W1569103839 cites W2067380602 @default.
• W1569103839 cites W2068663186 @default.
• W1569103839 cites W2072171435 @default.
• W1569103839 cites W2073492006 @default.
• W1569103839 cites W2074325744 @default.
• W1569103839 cites W2078541212 @default.
• W1569103839 cites W2088657149 @default.
• W1569103839 cites W2089737752 @default.
• W1569103839 cites W2095717214 @default.
• W1569103839 cites W2095880274 @default.
• W1569103839 cites W2096358774 @default.
• W1569103839 cites W2097599147 @default.
• W1569103839 cites W2103833664 @default.
• W1569103839 cites W2112238126 @default.
• W1569103839 cites W2117930227 @default.
• W1569103839 cites W2118045199 @default.
• W1569103839 cites W2123895705 @default.
• W1569103839 cites W2129456158 @default.
• W1569103839 cites W2130815592 @default.
• W1569103839 cites W2133659352 @default.
• W1569103839 cites W2137613347 @default.
• W1569103839 cites W2137825339 @default.
• W1569103839 cites W2140067651 @default.
• W1569103839 cites W2141959953 @default.
• W1569103839 cites W2145713350 @default.
• W1569103839 cites W2149099097 @default.
• W1569103839 cites W2155503635 @default.
• W1569103839 cites W2160771609 @default.
• W1569103839 cites W2161974745 @default.
• W1569103839 cites W2162576764 @default.
• W1569103839 cites W2164834273 @default.
• W1569103839 cites W2170766506 @default.
• W1569103839 cites W2323638458 @default.
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• W1569103839 doi "https://doi.org/10.1155/2015/376716" @default.
• W1569103839 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4515265" @default.
• W1569103839 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26246834" @default.
• W1569103839 hasPublicationYear "2015" @default.
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