인공지능 기반 평가 도구를 이용한 한의사의 체질 진단 평가 및 활용 방안에 대한 연구
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 박무순 | - |
dc.contributor.author | 황민우 | - |
dc.contributor.author | 이미숙 | - |
dc.contributor.author | 김창업 | - |
dc.contributor.author | 권영규 | - |
dc.date.accessioned | 2022-05-17T02:40:07Z | - |
dc.date.available | 2022-05-17T02:40:07Z | - |
dc.date.created | 2022-05-17 | - |
dc.date.issued | 2022-04 | - |
dc.identifier.issn | 1738-7698 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/84317 | - |
dc.description.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. | - |
dc.language | 한국어 | - |
dc.language.iso | ko | - |
dc.publisher | 한의병리학회 | - |
dc.relation.isPartOf | 동의생리병리학회지 | - |
dc.title | 인공지능 기반 평가 도구를 이용한 한의사의 체질 진단 평가 및 활용 방안에 대한 연구 | - |
dc.title.alternative | Research on the Evaluation and Utilization of Constitutional Diagnosis by Korean Doctors using AI-based Evaluation Tool | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 2 | - |
dc.identifier.bibliographicCitation | 동의생리병리학회지, v.36, no.2, pp.73 - 78 | - |
dc.identifier.kciid | ART002836657 | - |
dc.description.isOpenAccess | N | - |
dc.citation.endPage | 78 | - |
dc.citation.startPage | 73 | - |
dc.citation.title | 동의생리병리학회지 | - |
dc.citation.volume | 36 | - |
dc.citation.number | 2 | - |
dc.contributor.affiliatedAuthor | 김창업 | - |
dc.subject.keywordAuthor | Korean medicine | - |
dc.subject.keywordAuthor | Decision-making process | - |
dc.subject.keywordAuthor | Sasang Constitutional Medicine | - |
dc.subject.keywordAuthor | Knowledge extraction | - |
dc.subject.keywordAuthor | Random Forest | - |
dc.description.journalRegisteredClass | kci | - |
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