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JKM > Volume 46(2); 2025 > Article
Kim, Lee, Kim, and Nam: Construction of Korean Medicine Knowledge Graph and Development of Prescription Recommendation System based on RippleNet

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.

Fig. 1
Progress of the preference propagation in knowledge graph
jkm-46-2-51f1.gif
Fig. 2
Example of Mawhang-tang in Korean medicine knowledge graph
jkm-46-2-51f2.gif
Fig. 3
AUC variation graph of RippleNet algorithm parameters
jkm-46-2-51f3.gif
Fig. 4
Screenshot of the prescription recommendation system
jkm-46-2-51f4.gif
Table 1
Types and the Number of Nodes in Knowledge Graph
노드 타입 노드 개수
처방 2,466
약재 534
성분 6,713
타겟 4,221
질병 3,043
Table 2
AUC and ACC for Different Hop Numbers
홉 개수 AUC ACC
2 0.908 0.846
3 0.913 0.828
4 0.919 0.848
5 0.914 0.827
6 0.917 0.819
Table 3
AUC and ACC for Different Sizes of Ripple Set
리플셋 크기 AUC ACC
8 0.882 0.799
16 0.898 0.826
32 0.919 0.848
64 0.910 0.832
128 0.887 0.807
Table 4
AUC and ACC for Different Embedding Dimensions
임베딩 차원 AUC ACC
4 0.890 0.819
8 0.919 0.848
16 0.569 0.542
32 0.552 0.504
Table 5
AUC and ACC for Different Batch Sizes
배치 크기 AUC ACC
32 0.919 0.848
64 0.900 0.812
128 0.877 0.794
256 0.874 0.795
Table 6
AUC and ACC for Different L2 Regularization Weights
L2 정규화 계수 AUC ACC
10-3 0.847 0.753
10-4 0.879 0.795
10-5 0.919 0.848
10-6 0.856 0.794
10-7 0.635 0.581
Table 7
AUC and ACC for Different KGE Weights
KGE 계수 AUC ACC
0.01 0.898 0.811
0.02 0.915 0.847
0.03 0.919 0.848
0.04 0.902 0.823
0.05 0.897 0.813
Table 8
AUC and ACC for Different Learning Rates
학습률 AUC ACC
0.01 0.842 0.768
0.02 0.870 0.797
0.03 0.890 0.802
0.04 0.884 0.780
0.05 0.919 0.848
0.06 0.908 0.824
0.07 0.885 0.814

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