Development of a machine learning-based predictive model for long-term adverse outcomes in neonatal bacterial meningitis
Machine learning models, particularly the superior-performing Random Forest, are proven to reliably predict long-term adverse outcomes in NBM patients, aiding in the identification of high-risk individuals. Further validation in broader cohorts is warranted to enhance generalizability and clinical applicability.
Authors
- Peicen Zou (ORCID: https://orcid.org/0000-0001-7407-3227)
- Jing Wu (ORCID: https://orcid.org/0000-0003-1931-4678)
- Chi Wang (ORCID: https://orcid.org/0000-0002-0587-436X)
- Na Zhang (ORCID: https://orcid.org/0000-0003-2270-3463)
- Ying Li (ORCID: https://orcid.org/0000-0002-0882-5926)
- Huiguang He (ORCID: https://orcid.org/0000-0002-0684-1711)
- Yajuan Wang (ORCID: https://orcid.org/0000-0002-8830-7255)
- Ruiqi Xiao (ORCID: https://orcid.org/0000-0002-0445-7791)
- Shengpei Wang (ORCID: https://orcid.org/0000-0001-6032-9578)
- Ying Chen (ORCID: https://orcid.org/0009-0000-4812-7658)
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Chinese PLA General Hospital (CN)
- Center for Excellence in Brain Science and Intelligence Technology (CN)
- Capital Institute of Pediatrics (CN)
Publication Details
- Journal
- Jornal de Pediatria
- Published
- 2025-11-21
- DOI
- https://doi.org/10.1016/j.jped.2025.101472
- Citations
- 2
- Primary Topic
- Bacterial Infections and Vaccines
- Type
- article
- Field-Weighted Citation Impact
- 2.37