A Survey on Knowledge Graphs for Healthcare: Resources, Applications, and Promises
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.
