Journal of Physiology & Pathology in Korean Medicine (동의생리병리학회지)

The Physiological Society of Korean Medicine and The Society of Pathology in Korean Medicine (대한동의생리학회·한의병리학회)
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Research on the Evaluation and Utilization of Constitutional Diagnosis by Korean Doctors using AI-based Evaluation Tool

인공지능 기반 평가 도구를 이용한 한의사의 체질 진단 평가 및 활용 방안에 대한 연구

  • Park, Musun (KM Data Division, Korea Institute of Oriental Medicine) ;
  • Hwang, Minwoo (Department of Sasang Constitutional Medicine, College of Korean Medicine, Kyung Hee University) ;
  • Lee, Jeongyun (Department of Sasang Constitutional Medicine, Division of Clinical Medicine 4, School of Korean Medicine, Pusan National University) ;
  • Kim, Chang-Eop (Department of Physiology, College of Korean Medicine, Gachon University) ;
  • Kwon, Young-Kyu (Division of Longevity and Biofunctional Medicine, School of Korean Medicine, Pusan National University)
  • 박무순 (한국한의학연구원 한의약데이터부) ;
  • 황민우 (경희대학교 한의과대학 사상체질과) ;
  • 이정윤 (부산대학교 한의학전문대학원 임상의학4교실 사상체질과) ;
  • 김창업 (가천대학교 한의과대학 생리학교실) ;
  • 권영규 (부산대학교 한의학전문대학원 양생기능의학교실)
  • Received : 2021.12.14
  • Accepted : 2022.03.16
  • Published : 2022.04.25
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Abstract

Since Traditional Korean medicine (TKM) doctors use various knowledge systems during treatment, diagnosis results may differ for each TKM doctor. However, it is difficult to explain all the reasons for the diagnosis because TKM doctors use both explicit and implicit knowledge. In this study, an upgraded random forest (RF)-based evaluation tool was proposed to extract clinical knowledge of TKM doctors. Also, it was confirmed to what extent the professor's clinical knowledge was delivered to the trainees by using the evaluation tool. The data used to construct the evaluation tool were targeted at 106 people who visited the Sasang Constitutional Department at Kyung Hee University Korean Medicine Hospital at Gangdong. For explicit knowledge extraction, four TKM doctors were asked to express the importance of symptoms as scores. In addition, for implicit knowledge extraction, importance score was confirmed in the RF model that learned the patient's symptoms and the TKM doctor's constitutional determination results. In order to confirm the delivery of clinical knowledge, the similarity of symptoms that professors and trainees consider important when discriminating constitution was calculated using the Jaccard coefficient. As a result of the study, our proposed tool was able to successfully evaluate the clinical knowledge of TKM doctors. Also, it was confirmed that the professor's clinical knowledge was delivered to the trainee. Our tool can be used in various fields such as providing feedback on treatment, education of training TKM doctors, and development of AI in TKM.

Keywords

Acknowledgement

이 논문은 2020년 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 기본연구사업(No. 2020R1F1A1075145), 그리고 한국한의학연구원 'AI 한의사 개발을 위한 임상 빅데이터 수집 및 서비스 플랫폼 구축 (KSN2021110)' 과제의 지원을 받아 수행된 연구임.

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