Acute kidney disease in hospitalized pediatric patients: risk prediction based on an artificial intelligence approach
The high incidence of AKI and AKD among hospitalized children warrants attention. Renal function trajectories are strongly associated with prognosis. Supported by a web-based tool, machine learning models can effectively predict AKI and AKD, facilitating early identification of high-risk pediatric patients and potentially improving outcomes.
Authors
- Ningxin Zhang
- Lin Che (ORCID: https://orcid.org/0009-0005-2619-9382)
- Chen Guan (ORCID: https://orcid.org/0000-0002-3709-1247)
- Yan Xu (ORCID: https://orcid.org/0000-0003-2049-3976)
- Shuang Gao (ORCID: https://orcid.org/0000-0001-5988-9287)
- Chenyu Li (ORCID: https://orcid.org/0000-0002-5402-584X)
- Yanfei Wang (ORCID: https://orcid.org/0000-0002-4797-7498)
- Xinyuan Wang (ORCID: https://orcid.org/0000-0002-4378-7566)
- Lingyu Xu
- Xue Gao (ORCID: https://orcid.org/0000-0003-4805-6081)
- Tianyang Li
- Siqi Jiang
Institutions
- Qingdao University (CN)
- LMU Klinikum (DE)
- Affiliated Hospital of Qingdao University (CN)
- Ocean University of China (CN)
Publication Details
- Journal
- Renal Failure
- Published
- 2024-12-12
- DOI
- https://doi.org/10.1080/0886022x.2024.2438858
- Citations
- 2
- Primary Topic
- Acute Kidney Injury Research
- Type
- article
- Field-Weighted Citation Impact
- 0.68
Funders
- Qingdao Key Health Discipline Development Fund
- National Natural Science Foundation of China