AbstractObjectivesWe aim to propose an artificial intelligence system for recommending Korean medicine prescriptions based on the RippleNet algorithm using a knowledge graph.
MethodsThe 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.
ResultsThe 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.
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