文献类型：期刊文献

英文题名：Improving TCM Prescription Recommendation with Diffusion and Asymmetric Loss

作者：Ye, Lei[1]; Ma, Shanjie[1]; Li, Jianhua[1]

机构：[1] East China University Of Science and Technology, School of Information Science and Engineering, Shanghai, China

年份：2024

起止页码：1732

外文期刊名：Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

收录：EI(收录号：20250717854181)

语种：英文

外文关键词：Convolutional neural networks - Disease control - Graph embeddings - Graph neural networks - Medicinal chemistry - Multilayer neural networks - Network embeddings

摘要：Traditional Chinese Medicine (TCM) utilizes syndrome differentiation to formulate effective herbal prescriptions for treating complex diseases. Recently, some methods leveraging knowledge graph embedding and graph neural networks have shown promising performance. However, these approaches still straggle with challenges related to data quality, including sparsity and noise in knowledge graphs, as well as imbalances between positive and negative samples in datasets, which hinder performance improvements. To address these issues, our study introduces a novel approach integrating the Diffusion-Augmented Knowledge Graph Embedding and Graph Convolutional Network (GCN) with Asymmetric Loss (ASL). Firstly, we enhance herb representation by combining the Diffusion model with knowledge graph embedding to effectively mitigate noise and sparsity issues inherent in the knowledge graph. Then, we construct a TCM symptom-herb bipartite graph based on the dataset and employ a two-layer Bipartite Graph Convolutional Neural Network (Bipar GCN) to obtain feature embeddings. To tackle the imbalance between positive and negative samples, we introduce ASL to optimize the model. Finally, we aggregate symptom embeddings using MLP to derive a syndrome embedding, which is then multiplied with herb embeddings to compute prediction scores. Evaluated on a TCM prescription benchmark, our method outperforms others in Precision, Recall, and NDCG metrics, marking significant improvements in TCM prescription recommendation. ? 2024 IEEE.