中医药导报2025，Vol.31Issue(4)：231-237,7.DOI:10.13862/j.cn43-1446/r.2025.04.044
融合多视图图对比学习的中医脑卒中个性化处方推荐模型
A Personalized Prescription Recommendation Model for Stroke in Traditional Chinese Medicine by Integrating Multi-View Graph Contrastive Learning
摘要
Abstract
Objective:To construct a personalized prescription recommendation model that integrates multi-view graph contrastive learning(MVCL)based on real-world stroke clinical data.Methods:The stroke clinical dataset is modeled as a multi-graph structure,combining the properties of traditional Chinese medicine(TCM)and symptom semantic information to generate node embeddings with TCM characteristics.Subsequently,a similar view generator is proposed for view augmentation,and local contrastive learning is conducted separately in the symptom space and the TCM space to enable the model to uncover the compatibility patterns in the data better,thereby improving the accuracy of prescription recommendation.Results:The results of the comparative experiment on the stroke dataset show that compared with the relatively better performing SMRGAT,MVCL has improved by 5.01%,5.43%,and 5.30%respectively in the Precision@5,Recall@5,and F1-Score@5 indicators.The results of the comparative experiment on the public dataset(PTM)show that MVCL has improved by 7.92%,9.94%,and 9.08%respectively in the Precision@5,Recall@5,and F1-Score@5 indicators.Conclusion:The MVCL model shows advantages in both the accuracy of recommendation and the generalization ability of the model,providing more reliable support and new research ideas for the auxiliary diagnosis and treatment decision-making of stroke in traditional Chinese medicine.关键词
脑卒中/中医处方推荐/推荐模型/多视图/图对比学习/辅助诊疗Key words
stroke/prescription recommendation in traditional Chinese medicine/recommendation model/multi-view/graph contrastive learning/auxiliary diagnosis and treatment分类
医药卫生引用本文复制引用
赵紫一,丁长松..融合多视图图对比学习的中医脑卒中个性化处方推荐模型[J].中医药导报,2025,31(4):231-237,7.基金项目
湖南省中医药管理局重点课题(A2024011) （A2024011）
湖南省自然科学基金项目(2023JJ60124) （2023JJ60124）
湖南省教育厅科学研究重点项目(22A0255) （22A0255）
长沙市自然科学基金项目(kq2202265) （kq2202265）
