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A Real-World Evidence Study for Distribution of Traditional Chinese Medicine Syndrome and Its Elements on Respiratory Disease
BACKGROUND: This study aimed to investigate the distribution and characteristics of traditional Chinese medicine (TCM) syndrome and its elements on respiratory diseases (RDs) based on real-world data (RWD). METHODS: A real-world study was performed to explore the relationships among TCM syndrome and...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311270/ https://www.ncbi.nlm.nih.gov/pubmed/30643538 http://dx.doi.org/10.1155/2018/8305892 |
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author | Xu, Fei Cui, Wengqiang Kong, Qing Tang, Zihui Dong, Jingcheng |
author_facet | Xu, Fei Cui, Wengqiang Kong, Qing Tang, Zihui Dong, Jingcheng |
author_sort | Xu, Fei |
collection | PubMed |
description | BACKGROUND: This study aimed to investigate the distribution and characteristics of traditional Chinese medicine (TCM) syndrome and its elements on respiratory diseases (RDs) based on real-world data (RWD). METHODS: A real-world study was performed to explore the relationships among TCM syndrome and RDs based on electronic medical information. A total of 26,074 medical records with complete data were available for data analysis. Factor analyses were used to reduce dimensions of TCM syndrome elements and detect common factors. Additionally, cluster analyses were employed to assess combinations of TCM syndrome elements. Finally, association rule analyses were performed to investigate the structures of TCM syndrome elements to estimate the patterns of TCM syndrome. RESULTS: A total of 27 TCM syndromes were extracted from RWD in this work. There were four TCM syndromes with >5.0% frequency based on the distribution frequency. The top five pathogenesis TCM syndrome elements were Tan, Huo, Feng, Qi_Xu, and Han. Factor analysis, cluster analysis, and association rule analysis demonstrated that Tan, Huo, Feng, Qi_Xu, Shen, and Fei were the core TCM syndrome elements. CONCLUSION: Four common Shi TCM syndromes on RDs were identified: Tan_Re_Yong_Fei, Tan_Zhuo_Zu_Fei, Feng_Re_Fan_Fei, and Feng_Han_Xi_Fei; two core common Xu TCM syndromes (Fei_Shen_Qi_Xu and Fei_Yin_Xu) and two core common Mix TCM syndromes (Fei_Pi_Qi_Xu-Tan_Shi_Yun_Fei and Fei_Shen_Qi_Xu-Tan_Yu_Zu_Fei) were also determined. The core TCM syndrome elements of Tan, Huo, Feng, Qi_Xu, Shen, and Fei were identified in this work. |
format | Online Article Text |
id | pubmed-6311270 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-63112702019-01-14 A Real-World Evidence Study for Distribution of Traditional Chinese Medicine Syndrome and Its Elements on Respiratory Disease Xu, Fei Cui, Wengqiang Kong, Qing Tang, Zihui Dong, Jingcheng Evid Based Complement Alternat Med Research Article BACKGROUND: This study aimed to investigate the distribution and characteristics of traditional Chinese medicine (TCM) syndrome and its elements on respiratory diseases (RDs) based on real-world data (RWD). METHODS: A real-world study was performed to explore the relationships among TCM syndrome and RDs based on electronic medical information. A total of 26,074 medical records with complete data were available for data analysis. Factor analyses were used to reduce dimensions of TCM syndrome elements and detect common factors. Additionally, cluster analyses were employed to assess combinations of TCM syndrome elements. Finally, association rule analyses were performed to investigate the structures of TCM syndrome elements to estimate the patterns of TCM syndrome. RESULTS: A total of 27 TCM syndromes were extracted from RWD in this work. There were four TCM syndromes with >5.0% frequency based on the distribution frequency. The top five pathogenesis TCM syndrome elements were Tan, Huo, Feng, Qi_Xu, and Han. Factor analysis, cluster analysis, and association rule analysis demonstrated that Tan, Huo, Feng, Qi_Xu, Shen, and Fei were the core TCM syndrome elements. CONCLUSION: Four common Shi TCM syndromes on RDs were identified: Tan_Re_Yong_Fei, Tan_Zhuo_Zu_Fei, Feng_Re_Fan_Fei, and Feng_Han_Xi_Fei; two core common Xu TCM syndromes (Fei_Shen_Qi_Xu and Fei_Yin_Xu) and two core common Mix TCM syndromes (Fei_Pi_Qi_Xu-Tan_Shi_Yun_Fei and Fei_Shen_Qi_Xu-Tan_Yu_Zu_Fei) were also determined. The core TCM syndrome elements of Tan, Huo, Feng, Qi_Xu, Shen, and Fei were identified in this work. Hindawi 2018-12-12 /pmc/articles/PMC6311270/ /pubmed/30643538 http://dx.doi.org/10.1155/2018/8305892 Text en Copyright © 2018 Fei Xu et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Xu, Fei Cui, Wengqiang Kong, Qing Tang, Zihui Dong, Jingcheng A Real-World Evidence Study for Distribution of Traditional Chinese Medicine Syndrome and Its Elements on Respiratory Disease |
title | A Real-World Evidence Study for Distribution of Traditional Chinese Medicine Syndrome and Its Elements on Respiratory Disease |
title_full | A Real-World Evidence Study for Distribution of Traditional Chinese Medicine Syndrome and Its Elements on Respiratory Disease |
title_fullStr | A Real-World Evidence Study for Distribution of Traditional Chinese Medicine Syndrome and Its Elements on Respiratory Disease |
title_full_unstemmed | A Real-World Evidence Study for Distribution of Traditional Chinese Medicine Syndrome and Its Elements on Respiratory Disease |
title_short | A Real-World Evidence Study for Distribution of Traditional Chinese Medicine Syndrome and Its Elements on Respiratory Disease |
title_sort | real-world evidence study for distribution of traditional chinese medicine syndrome and its elements on respiratory disease |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311270/ https://www.ncbi.nlm.nih.gov/pubmed/30643538 http://dx.doi.org/10.1155/2018/8305892 |
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