A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2023
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866916593973329920 |
|---|---|
| author | Cui, Hejie Lu, Jiaying Xu, Ran Wang, Shiyu Ma, Wenjing Yu, Yue Yu, Shaojun Kan, Xuan Ling, Chen Zhao, Liang Qin, Zhaohui S. Ho, Joyce C. Fu, Tianfan Ma, Jing Huai, Mengdi Wang, Fei Yang, Carl |
| author_facet | Cui, Hejie Lu, Jiaying Xu, Ran Wang, Shiyu Ma, Wenjing Yu, Yue Yu, Shaojun Kan, Xuan Ling, Chen Zhao, Liang Qin, Zhaohui S. Ho, Joyce C. Fu, Tianfan Ma, Jing Huai, Mengdi Wang, Fei Yang, Carl |
| contents | This comprehensive review aims to provide an overview of the current state of Healthcare Knowledge Graphs (HKGs), including their construction, utilization models, and applications across various healthcare and biomedical research domains. We thoroughly analyzed existing literature on HKGs, covering their construction methodologies, utilization techniques, and applications in basic science research, pharmaceutical research and development, clinical decision support, and public health. The review encompasses both model-free and model-based utilization approaches and the integration of HKGs with large language models (LLMs). We searched Google Scholar for relevant papers on HKGs and classified them into the following topics: HKG construction, HKG utilization, and their downstream applications in various domains. We also discussed their special challenges and the promise for future work. The review highlights the potential of HKGs to significantly impact biomedical research and clinical practice by integrating vast amounts of biomedical knowledge from multiple domains. The synergy between HKGs and LLMs offers promising opportunities for constructing more comprehensive knowledge graphs and improving the accuracy of healthcare applications. HKGs have emerged as a powerful tool for structuring medical knowledge, with broad applications across biomedical research, clinical decision-making, and public health. This survey serves as a roadmap for future research and development in the field of HKGs, highlighting the potential of combining knowledge graphs with advanced machine learning models for healthcare transformation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_04802 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises Cui, Hejie Lu, Jiaying Xu, Ran Wang, Shiyu Ma, Wenjing Yu, Yue Yu, Shaojun Kan, Xuan Ling, Chen Zhao, Liang Qin, Zhaohui S. Ho, Joyce C. Fu, Tianfan Ma, Jing Huai, Mengdi Wang, Fei Yang, Carl Artificial Intelligence Computation and Language Machine Learning Social and Information Networks 68T30, 68T50, 68T09 I.2.4; I.2.6; I.2.7; J.3 This comprehensive review aims to provide an overview of the current state of Healthcare Knowledge Graphs (HKGs), including their construction, utilization models, and applications across various healthcare and biomedical research domains. We thoroughly analyzed existing literature on HKGs, covering their construction methodologies, utilization techniques, and applications in basic science research, pharmaceutical research and development, clinical decision support, and public health. The review encompasses both model-free and model-based utilization approaches and the integration of HKGs with large language models (LLMs). We searched Google Scholar for relevant papers on HKGs and classified them into the following topics: HKG construction, HKG utilization, and their downstream applications in various domains. We also discussed their special challenges and the promise for future work. The review highlights the potential of HKGs to significantly impact biomedical research and clinical practice by integrating vast amounts of biomedical knowledge from multiple domains. The synergy between HKGs and LLMs offers promising opportunities for constructing more comprehensive knowledge graphs and improving the accuracy of healthcare applications. HKGs have emerged as a powerful tool for structuring medical knowledge, with broad applications across biomedical research, clinical decision-making, and public health. This survey serves as a roadmap for future research and development in the field of HKGs, highlighting the potential of combining knowledge graphs with advanced machine learning models for healthcare transformation. |
| title | A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises |
| topic | Artificial Intelligence Computation and Language Machine Learning Social and Information Networks 68T30, 68T50, 68T09 I.2.4; I.2.6; I.2.7; J.3 |
| url | https://arxiv.org/abs/2306.04802 |