数字中医药（英文）2026，Vol.9Issue(3)：362-374,13.DOI:10.1016/j.dcmed.2026.08.004
TCM-HyperRAG:面向中医疾病诊断和处方推荐的高阶知识超图
TCM-HyperRAG:higher-order knowledge hypergraphs for disease diagnosis and prescription recommendation in traditional Chinese medicine
摘要
Abstract
Objective To address the limitations of knowledge graphs in modeling higher-order associa-tions in traditional Chinese medicine(TCM)knowledge and distinguishing the importance of different entities,we propose a weighted knowledge hypergraph-enhanced framework for in-telligent assistance in TCM diagnosis and prescription recommendation. Methods We developed TCM-HyperRAG,a weighted knowledge hypergraph-based method,and validated it using a publicly available TCM Supervised Fine-Tuning(TCM-SFT)dataset.DeepSeek-V3 extracted disease/syndrome,symptom,and herb entities from 18 901 disease-diagnosis records and 9 290 prescription-recommendation records to construct the weighted hypergraph.The corresponding retrieval strategy then provided the backbone large language models(LLMs)with structured and traceable evidence.The framework was tested on two held-out sets of 500 previously unseen records for disease diagnosis and prescription recom-mendation,respectively.LLM-only inference,standard RAG,four GraphRAG modes,and TCM-HyperRAG were compared using HuatuoGPT-o1,Llama3.1-8B,Qwen3-8B,GLM4-9B,DeepSeek-V3,and Qwen3-Max as backbone LLMs.Disease diagnosis was evaluated using mean reciprocal rank(MRR)and hit rate at k(H@k,k ∈{1,3,5}),whereas prescription rec-ommendation was evaluated using precision at k(P@k),recall at k(R@k),and F1 score at k(F1@k),where k ∈{5,10,20}.Repeated experiments assessed performance stability,and ab-lation experiments examined the contribution of the importance weights. Results TCM-HyperRAG achieved competitive results on both tasks.For disease diagnosis,it obtained the highest MRR with Llama3.1-8B,GLM4-9B,DeepSeek-V3,and Qwen3-Max,and an average MRR of 0.638,higher than 0.571 for LLM-only inference and 0.486 for the GraphRAG variants.For prescription recommendation,TCM-HyperRAG achieved the high-est R@20 with all six backbone LLMs,averaging 0.544 versus 0.496 for GraphRAG+Mix.In the repeated experiments with Qwen3-Max,TCM-HyperRAG obtained the highest mean MRR,H@3,and H@5,reaching 0.769 1,0.862 5,and 0.911 8,respectively,indicating stable perfor-mance.Removing entity importance weights reduced the average disease-diagnosis MRR from 0.638 to 0.629 and the average prescription-recommendation F1@10 from 0.438 to 0.425,indicating a positive overall contribution from importance-aware weighting. Conclusion By preserving multi-entity relations and prioritizing evidence with greater clini-cal importance,TCM-HyperRAG achieves competitive performance in disease diagnosis and prescription recommendation while providing traceable evidence for LLM-based intelligent TCM decision support.关键词
中医药/智能诊断/处方推荐/检索增强生成/超图Key words
Traditional Chinese medicine/Intelligent diagnosis/Prescription recommendation/Retrieval-augmented generation/Hypergraphs引用本文复制引用
陈伟林,叶绍强,周辉,康棣文,秦莉花,周恺卿..TCM-HyperRAG:面向中医疾病诊断和处方推荐的高阶知识超图[J].数字中医药（英文）,2026,9(3):362-374,13.基金项目
National Natural Science Foundation of China(82374437),Natural Science Foundation of Hunan Province(2024JJ7395),and Hunan Province Traditional Chinese Medicine Scientific Research Project(A2024001). （82374437）
