Knowledge Graph Representation in Medical Era-An Analysis

Abstract

Healthcare knowledge graphs (HKGs) are considered as a 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.Here summarize the pipeline and key techniques for HKG construction, as well as the common utilization approaches (i.e., model free and model-based). To provide researchers with valuable resources, we organize existing HKGs1 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