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Original Article
한약 처방 논문의 증거 수준 예측을 위한 딥러닝 모델 개발
Development of a Deep Learning Model to Predict the Evidence Levels of Research Papers Addressing Herbal Medicine Formula
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- 발행기관
- 한약정보연구회 바로가기
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- 간행물
- 한약정보연구회지 KCI 등재후보 바로가기
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- 통권
- 제12권 제2호 (2024.12)바로가기
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- 페이지
- pp.105-114
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- 저자
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- 언어
- 한국어(KOR)
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- URL
- https://www.earticle.net/Article/A458813
원문정보
초록
- 영어
- Herbal medicine formula (HMF), a crucial treatment method in Korean medicine (KM), is increasingly being addressed in high-quality scientific journals, showing rapid growth both qualitatively and quantitatively. However, much valuable knowledge remains unstructured within a vast number of published papers. Recently, various studies have been conducted to extract knowledge from these unstructured papers by applying deep learning-based natural language processing (NLP) technologies. The levels of evidence, which indicate how reliable a particular study's findings are for making clinical decisions, play a crucial role in practicing evidence-based medicine. However, manually assessing the quality of research and determining its clinical applicability in the rapidly increasing number of papers related to HMF requires significant time and effort. Therefore, in this study, we aim to develop an algorithm to automatically determine the level of evidence of papers related to HMF by applying NLP methods. We constructed a corpus for AI training and testing. First, we selected 740 papers related to HMF and diseases randomly from PubMed using an HMF dictionary and a disease terminology dictionary. Experts in KM annotated the evidence levels to build the corpus. The distribution of evidence levels in the corpus was identified as follows: In-vivo Studies (61.22%), Randomized Control Trials (8.65%), and In-vitro Studies (7.84%). We fine-tuned BERT-based models with the built corpus to create a model that determines the evidence levels of a given paper. By evaluating the performance of four fine-tuned models, we found that SciBERT demonstrated the best performance with 94.59% (micro-F1), 89.11% (macro-F1), and 94.38% (weighted-F1).
목차
Abstract
서론
본론
1. 연구 방법
2. 연구 결과
3. 고찰
결론
감사의 글
참고문헌
키워드
herbal medicine formula levels of evidence deep learning latural language processing prediction algorithm저자
간행물 정보
발행기관
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- 발행기관명
- 한약정보연구회 [The Society of Korean Herbal Medicine Information]
- 설립연도
- 2013
- 분야
- 의약학>한의학
- 소개
- 한약(한약재 및 한약제제)과 관련된 정보를 연구하는 모임으로서 2013년에 발족하였으며, 문헌·도표·전산자료 등의 한약 관련 정보를 수집·생산하거나 취합·편집·요약·평가 등 재가공하여 공유하고 발표하는 것을 주된 사업으로 삼고 있습니다.
간행물
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- 간행물명
- 한약정보연구회지 [Korean Herbal Medicine Informatics]
- 간기
- 반년간
- pISSN
- 2288-5161
- eISSN
- 2288-5293
- 수록기간
- 2013~2026
- 십진분류
- KDC 519 DDC 610
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