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

박무순; 황민우; 이정윤; 김창업; 권영규

doi:10.15188/kjopp.2022.04.36.2.73

주제분류

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저널정보

한의병리학회
동의생리병리학회지 학술저널
동의생리병리학회지 제36권 제2호
2022.4 73 - 78 (6page)
DOI : 10.15188/kjopp.2022.04.36.2.73

저자정보

박무순 (한국한의학연구원)
황민우 (경희대학교)
이정윤 (부산대학교)
김창업 (가천대학교)
권영규 (부산대학교)

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초록·키워드

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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.

#Korean medicine #Decision-making process #Sasang Constitutional Medicine #Knowledge extraction #Random Forest