Machine learning model for predicting severe infection in children with idiopathic nephrotic syndrome: multicenter retrospective study
A LightGBM model for predicting severe infection in patients with INS had excellent performance. Future applications of this model may provide an effective, convenient, and cost-effective approach for early identification of severe infection in children with INS.
Authors
- Wenhao Tang (ORCID: https://orcid.org/0000-0002-6591-6584)
- Anshuo Wang
- Hongxing Chen (ORCID: https://orcid.org/0000-0002-4735-8777)
- Sijie Yu
- Zhang De (ORCID: https://orcid.org/0000-0003-1477-2557)
- Haiping Yang (ORCID: https://orcid.org/0000-0002-4245-9376)
- Xiao Li (ORCID: https://orcid.org/0000-0002-6630-0503)
- Fengguang Shen (ORCID: https://orcid.org/0009-0005-8093-7518)
- Li Wang (ORCID: https://orcid.org/0000-0002-0910-0361)
- Mo Wang (ORCID: https://orcid.org/0000-0002-4761-4216)
- Yan Li (ORCID: https://orcid.org/0000-0001-6021-1641)
- Qiu Li (ORCID: https://orcid.org/0000-0002-2481-7168)
- Fanghong Zhang (ORCID: https://orcid.org/0000-0003-0398-9902)
- Han Chen
- Z Chen
Institutions
- Chongqing Normal University (CN)
- Chongqing University (CN)
- Chengdu Women's and Children's Central Hospital (CN)
- Xuzhou Children Hospital (CN)
- Children's Hospital of Chongqing Medical University (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- The Italian Journal of Pediatrics/Italian journal of pediatrics
- Published
- 2025-11-25
- DOI
- https://doi.org/10.1186/s13052-025-02149-7
- Citations
- 2
- Primary Topic
- Renal Diseases and Glomerulopathies
- Type
- article
- Field-Weighted Citation Impact
- 2.86
Funders
- Chongqing Medical University
- Chongqing Municipal Education Commission