ArticleHeliyon2024
Analysis of medication rule of Traditional Chinese Medicine in treating depression based on data mining.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Nutrients and Functional Components of Medicine and Food Homology Substances on Antidepressant Effects: A Mechanism-Oriented Review.Molecules (Basel, Switzerland) · 2026Review
- Clinical efficacy and effects on hypothalamic-pituitary-adrenal axis function of proscar combined with selective serotonin reuptake inhibitor in post-stroke depression.World journal of psychiatry · 2026Article
- Association between the combination of GABAergic agents and SSRIs at the first clinical visit and depressive symptom trajectories: A study using group-based trajectory modeling and Apriori algorithm.PLOS mental health · 2026Article
- Prediction of herbal compatibility for colorectal adenoma treatment based on graph neural networks.Chinese medicine · 2025Article
- Understanding the ancient classic and famous prescriptionsFrontiers in pharmacology · 2025Article
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Authors and funding
6 authors.
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Abstract
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.
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