Analysis of medication rule of Traditional Chinese Medicine in treating depression based on data mining
Objective: Depression is a mental disorder characterized by persistent feelings of sadness, decreased interest or pleasure in activities and reduced energy. As a highly prevalent disorder, it seriously endangers the psychosocial functioning of patients. Many scholars have conducted clinical studies...
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| Language: | English |
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Elsevier
2024-10-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024152760 |
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| author | Jin Yang Shuai Wang Zhen Zhang Junjie Huang Weihai Chen Zhan Xu |
| author_facet | Jin Yang Shuai Wang Zhen Zhang Junjie Huang Weihai Chen Zhan Xu |
| author_sort | Jin Yang |
| collection | DOAJ |
| description | Objective: Depression is a mental disorder characterized by persistent feelings of sadness, decreased interest or pleasure in activities and reduced energy. As a highly prevalent disorder, it seriously endangers the psychosocial functioning of patients. Many scholars have conducted clinical studies on the treatment of depression using different herbal remedies, but there are no studies that integrate these remedies to explore the general medication rule. This study aims to explore the medication pattern of Traditional Chinese Medicine (TCM) treatment for depression through data mining methods, so as to provide scientific theoretical basis and reference for clinical treatment and new prescription development. Methods: Based on the PRISMA principle, 121 articles involving 10810 patients with depression of TCM treatment were collected. We then performed frequency, association rule, and hierarchical clustering analysis of Chinese herbs using Microsoft Excel 2016, SPSS Modeler 18.0 and IBM SPSS Statistics 23. Results: Among the 270 herbs collected, the three most frequently occurring herbs are Gancao, Chaihu, and Shaoyao. The categories of high-frequency herbs are mainly deficiency-tonifying, Qi-regulating and blood-activating and stasis-eliminating herbs. Through the Apriori algorithm, we mined 21 herbal groups of association rules, and among which the combination of Chaihu-Shaoyao-Gancao has the highest level of support. Furthermore, five novel clustering combinations were identified, predominantly derived from Xiaoyao-San, Chaihu-Shugan-San, Sini powder, Kaixin-San and Chaihu-Jia-Longgu-Muli Decoction. Conclusion: The current study not only concluded the frequent combinations but also developed five new drug cluster combinations for depression, which can provide evidence-based references for the future clinical treatment and is helpful to understand the potential pharmaceutical mechanism from the properties, tastes, meridian tropisms and categories. The clinical effectiveness of these combinations needs to be verified by future study. |
| format | Article |
| id | doaj-art-76d7ea0f70df4125a9a112f289f01f72 |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-76d7ea0f70df4125a9a112f289f01f722024-11-12T05:20:10ZengElsevierHeliyon2405-84402024-10-011020e39245Analysis of medication rule of Traditional Chinese Medicine in treating depression based on data miningJin Yang0Shuai Wang1Zhen Zhang2Junjie Huang3Weihai Chen4Zhan Xu5Faculty of Psychology, Southwest University, Chongqing, ChinaFaculty of Psychology, Southwest University, Chongqing, ChinaDepartment of Economics and Management, Southwest University, Chongqing, ChinaWesta College, Southwest University, Chongqing, ChinaFaculty of Psychology, Southwest University, Chongqing, China; Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, ChinaFaculty of Psychology, Southwest University, Chongqing, China; Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China; Corresponding author. Faculty of Psychology, Southwest University, Chongqing, China.Objective: Depression is a mental disorder characterized by persistent feelings of sadness, decreased interest or pleasure in activities and reduced energy. As a highly prevalent disorder, it seriously endangers the psychosocial functioning of patients. Many scholars have conducted clinical studies on the treatment of depression using different herbal remedies, but there are no studies that integrate these remedies to explore the general medication rule. This study aims to explore the medication pattern of Traditional Chinese Medicine (TCM) treatment for depression through data mining methods, so as to provide scientific theoretical basis and reference for clinical treatment and new prescription development. Methods: Based on the PRISMA principle, 121 articles involving 10810 patients with depression of TCM treatment were collected. We then performed frequency, association rule, and hierarchical clustering analysis of Chinese herbs using Microsoft Excel 2016, SPSS Modeler 18.0 and IBM SPSS Statistics 23. Results: Among the 270 herbs collected, the three most frequently occurring herbs are Gancao, Chaihu, and Shaoyao. The categories of high-frequency herbs are mainly deficiency-tonifying, Qi-regulating and blood-activating and stasis-eliminating herbs. Through the Apriori algorithm, we mined 21 herbal groups of association rules, and among which the combination of Chaihu-Shaoyao-Gancao has the highest level of support. Furthermore, five novel clustering combinations were identified, predominantly derived from Xiaoyao-San, Chaihu-Shugan-San, Sini powder, Kaixin-San and Chaihu-Jia-Longgu-Muli Decoction. Conclusion: The current study not only concluded the frequent combinations but also developed five new drug cluster combinations for depression, which can provide evidence-based references for the future clinical treatment and is helpful to understand the potential pharmaceutical mechanism from the properties, tastes, meridian tropisms and categories. The clinical effectiveness of these combinations needs to be verified by future study.http://www.sciencedirect.com/science/article/pii/S2405844024152760DepressionTraditional Chinese MedicineData miningMedication rule |
| spellingShingle | Jin Yang Shuai Wang Zhen Zhang Junjie Huang Weihai Chen Zhan Xu Analysis of medication rule of Traditional Chinese Medicine in treating depression based on data mining Heliyon Depression Traditional Chinese Medicine Data mining Medication rule |
| title | Analysis of medication rule of Traditional Chinese Medicine in treating depression based on data mining |
| title_full | Analysis of medication rule of Traditional Chinese Medicine in treating depression based on data mining |
| title_fullStr | Analysis of medication rule of Traditional Chinese Medicine in treating depression based on data mining |
| title_full_unstemmed | Analysis of medication rule of Traditional Chinese Medicine in treating depression based on data mining |
| title_short | Analysis of medication rule of Traditional Chinese Medicine in treating depression based on data mining |
| title_sort | analysis of medication rule of traditional chinese medicine in treating depression based on data mining |
| topic | Depression Traditional Chinese Medicine Data mining Medication rule |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024152760 |
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