Multicenter validation of an explainable machine learning model for early prediction of acute kidney injury in critically ill patients with digestive system tumors
- DOI
- 10.1177/20552076261461368
- Published
- 2026-02
- Container
- DIGITAL HEALTH
- Publisher
- SAGE Publications
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1177/20552076261461368,
title = {Multicenter validation of an explainable machine learning model for early prediction of acute kidney injury in critically ill patients with digestive system tumors},
author = {DunZhu Guo and Jing Bai and Jian Zhang and Xiuming Xi and YuJuan Chen and ZhiPeng Luo and Kai Feng and JiangWei Zeng and MengXin Zhang and WeiQin Dong and XinXin Xu and Rui Wang and Yu Zhang},
year = {2026},
journal = {DIGITAL HEALTH},
doi = {10.1177/20552076261461368},
url = {https://doi.org/10.1177/20552076261461368}
}RIS
TY - JOUR TI - Multicenter validation of an explainable machine learning model for early prediction of acute kidney injury in critically ill patients with digestive system tumors AU - DunZhu Guo AU - Jing Bai AU - Jian Zhang AU - Xiuming Xi AU - YuJuan Chen AU - ZhiPeng Luo AU - Kai Feng AU - JiangWei Zeng AU - MengXin Zhang AU - WeiQin Dong AU - XinXin Xu AU - Rui Wang AU - Yu Zhang PY - 2026 JO - DIGITAL HEALTH DO - 10.1177/20552076261461368 UR - https://doi.org/10.1177/20552076261461368 ER -
APA
Guo, D., Bai, J., Zhang, J., Xi, X., Chen, Y., Luo, Z., Feng, K., Zeng, J., Zhang, M., Dong, W., Xu, X., Wang, R., & Zhang, Y. (2026). Multicenter validation of an explainable machine learning model for early prediction of acute kidney injury in critically ill patients with digestive system tumors. DIGITAL HEALTH. https://doi.org/10.1177/20552076261461368
Source records
- crossref · retrieved 2026-09-26T21:48:52.088Z