Abstract
Background: The selection and combination of acupoints in acupuncture are largely based on clinical experience, resulting in variability among treatment protocols and difficulties in standardization. Recently, data mining has been applied to analyze acupoint combination patterns; however, existing studies remain fragmented across individual diseases and analytical methods, and the overall trends in data mining applications in acupuncture have not been clearly synthesized.
Objective: To synthesize and analyze studies applying data mining techniques to the analysis of acupoint combination patterns in acupuncture across different disease groups.
Methods: This study was conducted as a narrative literature review. Relevant studies on data mining applications in acupuncture were retrieved from scientific databases and categorized according to disease groups, data mining methods, and the core acupoints analyzed.
Results: Association rule mining was the most frequently used method in studies analyzing acupoint combination patterns, followed by cluster analysis and network analysis. The identified acupoint combinations showed relative consistency within individual disease groups, while also revealing the repeated occurrence of several core acupoints across multiple conditions, notably Hegu (LI4) and Zusanli (ST36), alongside acupoints specific to particular disease contexts.
Conclusion: This review provides an overall perspective on the trends and characteristics of data mining applications in acupuncture research, contributing to the systematic organization and synthesis of reported acupoint combination patterns.
| Published | 2026-09-28 | |
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| Issue | Vol. 16 No. 5 (2026) | |
| Section | Reviews | |
| DOI | 10.34071/jmp.2026.5.961 | |
| Keywords | acupuncture, data mining, acupoint combination patterns, narrative review châm cứu, khai thác dữ liệu, quy luật phối huyệt, tổng quan tài liệu |
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright (c) 2026 Hue Journal of Medicine and Pharmacy
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