CTKGR: temporal knowledge graph enhanced prediction of in-hospital events for pneumonia patients using electronic health records
The CTKGR model effectively captures the temporal dynamics inherent in clinical data, demonstrating robust predictive capabilities for dynamically evolving clinical events. This study offers a novel and effective framework for AI-driven clinical decision support, with strong potential for application in pneumonia management.
Authors
- Binyu Gao
- Congmin Zhu (ORCID: https://orcid.org/0000-0002-0472-9128)
- Chunting Tan (ORCID: https://orcid.org/0000-0002-6004-3390)
- Honglei Liu
- Pixian Yuan
- Yu Gu
- Peng Xie
- Yingliang Yang
Institutions
- Capital Medical University (CN)
- Southeast University (BD)
- Beijing Friendship Hospital (CN)
- Southeast University (CN)
Publication Details
- Journal
- Journal of Biomedical Informatics
- Published
- 2026-03-04
- DOI
- https://doi.org/10.1016/j.jbi.2026.105007
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00