A Review on Knowledge Graphs for Healthcare: Resources, Applications,
and Promises
- URL: http://arxiv.org/abs/2306.04802v3
- Date: Mon, 19 Feb 2024 23:40:58 GMT
- Title: A Review on Knowledge Graphs for Healthcare: Resources, Applications,
and Promises
- Authors: Hejie Cui, Jiaying Lu, Shiyu Wang, Ran Xu, Wenjing Ma, Shaojun Yu, Yue
Yu, Xuan Kan, Chen Ling, Tianfan Fu, Liang Zhao, Joyce Ho, Fei Wang, Carl
Yang
- Abstract summary: This work provides the first comprehensive review of healthcare knowledge graphs (HKGs)
It summarizes the pipeline and key techniques for HKG construction, as well as the common utilization approaches.
At the application level, we delve into the successful integration of HKGs across various health domains.
- Score: 53.48844796428081
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Healthcare knowledge graphs (HKGs) are valuable tools for organizing
biomedical concepts and their relationships with interpretable structures. The
recent advent of large language models (LLMs) has paved the way for building
more comprehensive and accurate HKGs. This, in turn, can improve the
reliability of generated content and enable better evaluation of LLMs. However,
the challenges of HKGs such as regarding data heterogeneity and limited
coverage are not fully understood, highlighting the need for detailed reviews.
This work provides the first comprehensive review of HKGs. It summarizes the
pipeline and key techniques for HKG construction, as well as the common
utilization approaches, i.e., model-free and model-based. The existing HKG
resources are also organized based on the data types they capture and
application domains they cover, along with relevant statistical information
(Resource available at
https://github.com/lujiaying/Awesome-HealthCare-KnowledgeBase). At the
application level, we delve into the successful integration of HKGs across
various health domains, ranging from fine-grained basic science research to
high-level clinical decision support and public health. Lastly, the paper
highlights the opportunities for HKGs in the era of LLMs. This work aims to
serve as a valuable resource for understanding the potential and opportunities
of HKG in health research.
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