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

초록

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.

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