인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
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.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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