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

통증 조절을 위한 경혈 선혈 패턴: 실사용데이터 데이터 마이닝

  • Da-Eun Yoon (Department of Meridian and Acupoints, College of Korean Medicine, Kyung Hee University) ;
  • Yoonjeong Seo (Department of Meridian Medical Science, Graduate School, Kyung Hee University) ;
  • Heeyoung Moon (Department of Meridian and Acupoints, College of Korean Medicine, Kyung Hee University) ;
  • Yeonhee Ryu (KM Science Research Division, Korea Institute of Oriental Medicine) ;
  • In-Seon Lee (Department of Meridian and Acupoints, College of Korean Medicine, Kyung Hee University) ;
  • Younbyoung Chae (Department of Meridian and Acupoints, College of Korean Medicine, Kyung Hee University)
  • 윤다은 (경희대학교 한의과대학 경혈학교실) ;
  • 서윤정 (경희대학교 일반대학원 경락의과학과) ;
  • 문희영 (경희대학교 한의과대학 경혈학교실) ;
  • 류연희 (한국한의학연구원 한의과학부) ;
  • 이인선 (경희대학교 한의과대학 경혈학교실) ;
  • 채윤병 (경희대학교 한의과대학 경혈학교실)
  • Received : 2024.09.23
  • Accepted : 2024.10.25
  • Published : 2024.10.25

Abstract

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.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00449485), and Korea Institute of Oriental Medicine (KSN1812181) and an Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korea government (MSIT) [RS-2022-00155911, Artificial Intelligence Convergence Innovation Human Resources Development (Kyung Hee University)]. We sincerely appreciate the contributions of the Korean Medical doctors who participated in this study. They are Yoonjeong Seo (KyungHee NARIN Korean Medicine Clinic), Shin Ho Kong (Misodam Korean Medicine Clinic), Changwoo Nam (Haenamu Korean Medicine Clinic), Joowon Hwang (Chungpoong Korean Medicine Clinic), Karam Kim (Kyunghee Ilsaeng Korean Medicine Clinic), Man-Heum Kwon (H-Nuri Korean Medicine Clinic), and Hyun-Woo Jin (Cleanwood Korean Medicine Clinic).

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