Korean medicine Data Center 한의학의 임상현상을 과학적으로 규명하기 위한 체계적 통합 정보은행
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제목 A Machine Learning Approach for Recommending Herbal Formulae with Enhanced Interpretability and Applicability
등록일 2022-11-28 첨부파일
구분 SCI
학술지 Biomolecules
발표일 2022-10-31
저자 이원융(동국대), 이영섭(한국한의학연구원), 김영우(동국대), 김지환(가천대), 이시우(한국한의학연구원)
Abstract: Herbal formulae (HFs) are representative interventions in Korean medicine (KM) for the prevention and treatment of various diseases. Here, we proposed a machine learning-based approach
for HF recommendation with enhanced interpretability and applicability. A dataset consisting of clinical symptoms, Sasang constitution (SC) types, and prescribed HFs was derived from a multicenter
study. Case studies published over 10 years were collected and curated by experts. Various classifiers, oversampling methods, and data imputation techniques were comprehensively considered. The local interpretable model-agnostic explanation (LIME) technique was applied to identify the clinical symptoms that led to the recommendation of specific HFs. We found that the cascaded deep forest (CDF) model with data imputation and oversampling yielded the best performance on the training set and holdout test set. Our model also achieved top-1 and top-3 accuracies of 0.35 and 0.89, respectively, on case study datasets in which clinical symptoms were only partially recorded. We performed an expert evaluation on the reliability of interpretation results using case studies and achieved a score close to normal. Taken together, our model will contribute to the modernization of KM and the identification of an HF selection process through the development of a practically useful HF
recommendation model.

*원문신청: kdc@kiom.re.kr