동의생리병리학회지 (Journal of Physiology & Pathology in Korean Medicine)

  • 제35권5호
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  • Pages.132-138
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  • 2021
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  • 1738-7698(pISSN)
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  • 2288-2529(eISSN)
대한동의생리학회·한의병리학회 ( The Physiological Society of Korean Medicine and The Society of Pathology in Korean Medicine)
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인공지능시대의 경혈 주치 연구를 위한 제언

Suggestions for the Study of Acupoint Indications in the Era of Artificial Intelligence

  • 채윤병 (경희대학교 한의과대학 침구경락융합연구센터)
  • Chae, Youn Byoung (Acupuncture & Meridian Science Research Center, College of Korean Medicine, Kyung Hee University)
  • 투고 : 2021.10.06
  • 심사 : 2021.10.22
  • 발행 : 2021.10.25
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초록

Artificial intelligence technology sheds light on new ways of innovating acupuncture research. As acupoint selection is specific to target diseases, each acupoint is generally believed to have a specific indication. However, the specificity of acupoint selection may be not always same with the specificity of acupoint indication. In this review, we propose that the specificity of acupoint indication can be inferred from clinical data using reverse inference. Using forward inference, the prescribed acupoints for each disease can be quantified for the specificity of acupoint selection. Using reverse inference, targeted diseases for each acupoint can be quantified for the specificity of acupoint indication. It is noteworthy that the selection of an acupoint for a particular disease does not imply the acupoint has specific indications for that disease. Electronic medical record includes various symptoms and chosen acupoint combinations. Data mining approach can be useful to reveal the complex relationships between diseases and acupoints from clinical data. Combining the clinical information and the bodily sensation map, the spatial patterns of acupoint indication can be further estimated. Interoperable medical data should be collected for medical knowledge discovery and clinical decision support system. In the era of artificial intelligence, machine learning can reveal the associations between diseases and prescribed acupoints from large scale clinical data warehouse.

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과제정보

This research was supported by Korea Institute of Oriental Medicine (KSN1812181) and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No. 2021R1F1A1046705).

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