文献类型：期刊文献

中文题名：融合子图结构的医学知识推理方法综述

英文题名：Overview of Medical Knowledge Inference Methods with Fusion Subgraph Structures

作者：宋豪俊[1];李燕[1];刘悦悦[1];何欣宇[1]

第一作者：宋豪俊

机构：[1]甘肃中医药大学医学信息工程学院,兰州730101

第一机构：甘肃中医药大学信息工程学院（教育技术中心）

年份：2025

卷号：46

期号：1

起止页码：63

中文期刊名：医学信息学杂志

外文期刊名：Journal of Medical Informatics

基金：中国高校产学研创新基金—蓝点分布式智能计算项目(项目编号:2021LDA09002)。

语种：中文

中文关键词：知识推理;子图结构;图神经网络;知识图谱

外文关键词：knowledge reasoning;subgraph structure;graph neural network;knowledge graph

摘要：目的/意义综述融合子图结构的医学知识图谱推理方法,为后续相关研究提供参考。方法/过程阅读并分析相关文献,结合知识图谱及其推理相关知识,分析目前融合子图结构的知识推理代表模型的特点和局限性,对比其与各类知识推理方法在相关领域任务中的优势和不足,总结归纳此类推理方法在医学领域的应用现状和未来发展前景。结果/结论未来研究应致力于探索医学领域内不同模态信息的交互关系,以丰富推理信息,从而构建更加完善的医学知识图谱,为临床实际问题提供有效解决方案。
Purpose/Significance To provide a comprehensive review of inference methods in medical knowledge graphs that incorporate subgraph structures,and to offer valuable insights for future research in the field.Method/Process A thorough analysis of relevant literature is conducted,integrating knowledge of knowledge graphs and inference techniques.The study highlights the features and limitations of current leading models that use subgraph structures for knowledge inference,and compares them with various other inference methods.The advantages and disadvantages of these approaches are assessed in the context of specific domain tasks.Furthermore,the current state of applications and future prospects of subgraph-based inference in the medical domain are summarized.Result/Conclusion Future studies should aim to explore the interaction between diverse modalities of information in the medical field to enhance inference capabilities.This will contribute to the development of more comprehensive medical knowledge graphs,thereby providing effective solutions to clinical practical challenges.