Data Mining-Based Analysis of Acupoint Selection Patterns in Acupuncture Treatment for Lumbar Disc Herniation

Area of Science:

• Integrative Medicine
• Clinical Data Mining
• Neuroscience

Background:

• Lumbar disc herniation (LDH) is a prevalent condition with significant global health impact.
• Current acupuncture treatments for LDH lack standardized acupoint selection, hindering consistent therapeutic outcomes.
• This research addresses the need for evidence-based acupoint standardization in acupuncture for LDH.

Purpose of the Study:

• To systematically identify core acupoint combinations for lumbar disc herniation (LDH) using data mining techniques.
• To uncover prevalent therapeutic strategies and acupoint-nerve relationships in acupuncture for LDH.
• To provide quantitative evidence for standardizing acupuncture protocols for LDH treatment.

Main Methods:

• Systematic literature search across seven databases (PubMed, Cochrane, Web of Science, etc.).
• Data mining including frequency analysis, association rule mining (Apriori algorithm), and hierarchical cluster analysis.
• Neural distribution analysis to map acupoint-nerve relationships.

Main Results:

• Analysis of 537 studies revealed 13 core acupoints, with Weizhong (BL40), Huantiao (GB30), and Dachangshu (BL25) being most frequent.
• The Weizhong (BL40)-Dachangshu (BL25) pair emerged as the strongest acupoint combination.
• Six distinct therapeutic strategies were identified, with the Bladder meridian cluster showing high clinical significance.

Conclusions:

• Data mining successfully identified core acupoint combinations and therapeutic strategies for acupuncture in LDH.
• The findings offer quantitative support for standardizing acupuncture treatment protocols for LDH.
• This research aids clinical decision-making by providing evidence-based acupoint selection for LDH.