Acupoint Selection Patterns of Acupoint for Pain Control: Data Mining from Real World Data

It is important to understand the underlying principles of acupoint selection for pain control. The purpose of this study was to use data mining on real-world data to examine the commonality and specificity of acupoint selection for pain control management. We obtained data from the medical records of eight Korean medicine doctors. We analyzed data on acupoint selection from 423 outpatients with seven different pain conditions including low back pain, migraine, irritable bowel syndrome, ankle sprain, knee pain, carpal tunnel syndrome, and dysmenorrhea. The frequency of acupoints used for pain control was calculated and visualized on a human body template to identify the patterns of acupoint selection regarding the type of pain and its location. The most frequently used acupoints across pain conditions were LI4, LR3, LI11, ST36, and PC6, while the most frequently used acupoints varied across individual pain conditions. In terms of the location of acupoints and disease site, both local and distal acupoints were used to treat pain. Patterns of selecting local or distal acupoints were observed differently by the types of pain such as visceral and somatic pain. Using data mining on real-world data, this study revealed the commonality and specificity of acupoint selection for pain control. Our findings suggest that local, segmental, and general effects of acupuncture can explain the selection patterns of acupoints for pain management.