Healthcare knowledge graphs (HKGs) have emerged as a promising tool for organizing medical knowledge in a structured and interpretable way, which provides a comprehensive view of medical concepts and their relationships. However, challenges such as data heterogeneity and limited coverage remain, emphasizing the need for further research in the field of HKGs. This survey paper serves as the first comprehensive overview of HKGs. We summarize the pipeline and key techniques for HKG construction (i.e., from scratch and through integration), as well as the common utilization approaches (i.e., model-free and model-based). To provide researchers with valuable resources, we organize existing HKGs (The resource is available at https://github.com/lujiaying/Awesome-HealthCare-KnowledgeBase) based on the data types they capture and application domains, supplemented with pertinent statistical information. In the application section, we delve into the transformative impact of HKGs across various healthcare domains, spanning from fine-grained basic science research to high-level clinical decision support. Lastly, we shed light on the opportunities for creating comprehensive and accurate HKGs in the era of large language models, presenting the potential to revolutionize healthcare delivery and enhance the interpretability and reliability of clinical prediction.

翻译：医疗保健知识图谱（HKGs）已成为以结构化、可解释方式组织医学知识的一种有前景的工具，能够提供医学概念及其关系的全面视图。然而，数据异构性及覆盖范围有限等挑战依然存在，凸显了在医疗保健知识图谱领域开展进一步研究的必要性。本综述论文是首个对医疗保健知识图谱进行全面概述的研究。我们总结了医疗保健知识图谱构建（即从零开始构建及通过集成构建）的流程与关键技术，以及常见的利用方法（即无模型方法与基于模型方法）。为向研究人员提供有价值的资源，我们根据现有医疗保健知识图谱所捕获的数据类型和应用领域进行了整理（资源见 https://github.com/lujiaying/Awesome-HealthCare-KnowledgeBase），并补充了相关统计信息。在应用部分，我们深入探讨了医疗保健知识图谱在从细粒度基础科学研究到高层次临床决策支持等多个医疗保健领域所产生的变革性影响。最后，我们揭示了大语言模型时代创建全面且精准的医疗保健知识图谱的机遇，阐述了其革新医疗保健服务、提升临床预测可解释性与可靠性的巨大潜力。