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About this paper
Hejie Cui, Jiaying Lu, Ran Xu, Shiyu Wang, Wenjing Ma, Yue Yu, Shaojun Yu, Xuan Kan and 9 more
This paper offers a comprehensive review of Healthcare Knowledge Graphs (HKGs), detailing their construction methods, utilization models, and diverse applications across biomedical research, pharmaceutical development, clinical decision support, and public health. It synthesizes existing literature, highlights the integration with Large Language Models (LLMs), and outlines future research directions.
Key insights
- HKGs provide a structured, interpretable framework to unify diverse medical data, addressing heterogeneity and enhancing clinical research and decision-making.
- Large Language Models (LLMs) play a transformative role, both in aiding HKG construction (e.g., entity/relation extraction) and being augmented by HKGs to reduce factual inaccuracies and ground outputs.
- HKGs are widely applicable across basic science research (medicinal chemistry, bioinformatics), pharmaceutical development (drug design, repurposing, clinical trials), clinical decision support (risk prediction, treatment recommendations), and public health (epidemiology, environmental health).
Summary by alphaXiv.
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