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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 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.
Author Information
Hejie Cui (Emory University)
I am a second-year Ph.D. student in Computer Science at Emory University, currently working with Dr. Carl Yang in Emory Graph Mining Lab. I have also been working closely with Dr. Eugene Agichtein in Emory Intelligent Information Access Lab (IR Lab). Before joining Emory, I got my bachelor’s degree in Software Engineering from Tongji University, where I was working with Dr. Lin Zhang. My current research interests lie in graph data mining and structured information systems.
Jiaying Lu (Emory University)
Shiyu Wang (Emory University)
Ran Xu (Emory University)
Wenjing Ma (Emory University)
Shaojun Yu (Emory University)
Yue Yu (Georgia Institute of Technology)
Xuan Kan (Emory University)
Tianfan Fu (Georgia Institute of Technology)
Chen Ling (Emory University)
Joyce Ho (Emory University)
Fei Wang (Cornell University)
Carl Yang (Emory University)
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