| 리플넷 알고리즘 기반 한의 지식 그래프 구축과 처방 추천 시스템 개발 |
김상균1 
, 이동형2
, 김안나1
, 남세진2
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1한국한의학연구원 한의약데이터부
2분당서울대학교병원 데이터융합팀 |
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| Construction of Korean Medicine Knowledge Graph and Development of Prescription Recommendation System based on RippleNet |
| Sang-Kyun Kim1
, DongHyoung Lee2
, Anna Kim1
, and SeJin Nam2
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1KM Data Division, Korea Institute of Oriental Medicine
2Data Convergence Team, Seoul National University Bundang Hospital |
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| Corresponding Author:
Sang-Kyun Kim ,Tel: +82-42-868-9526, Email: skkim@kiom.re.kr |
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Received: March 26, 2025; Revised: April 16, 2025. Accepted: May 22, 2025. |
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| ABSTRACT |
Objectives: We aim to propose an artificial intelligence system for recommending Korean medicine prescriptions based on the RippleNet algorithm using a knowledge graph.
Methods: The Korean medicine knowledge graph was constructed with information on prescriptions, medicinal materials, chemical compounds, gene targets, and modern diseases from the TM-MC database. The RippleNet algorithm was trained with the knowledge graph and the prescription history of diseases from TM-MC. The optimized hyperparameter values of the algorithm were derived using the grid search method. The recommendation system was implemented with the knowledge graph and the parameterized model.
Results: The Korean medicine knowledge graph contains 16,977 nodes and 142,924 edges, and the prescription histories of diseases include 2,101 true values and 3,413 false data points. The recommendation algorithm showed the best performance with an AUC of 0.919 when trained with parameters of 4 hops and a ripple set size of 32.
Conclusions: In this study, we implemented a system that recommends prescriptions based on artificial intelligence algorithms using the knowledge graph. In the future, we plan to improve the quality of the knowledge graph and explore a variety of modern AI algorithms to enhance recommendations. |
| Keywords:
Recommendation system | Prescription | Knowledge graph | RippleNet | Korean medicine |
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