Advancing risk factor identification for pediatric lobar pneumonia: the promise of machine learning technologies
The XGBoost model showed superior performance in predicting LP in children with CAP. The model enabled early diagnosis and risk assessment of LP, thereby facilitating appropriate clinical decision-making.
Authors
- Qiuyan Xu
- Yiguo Jiang (ORCID: https://orcid.org/0000-0003-1651-9926)
- Li Shen (ORCID: https://orcid.org/0000-0002-0069-1310)
- Xiaolan Zhang (ORCID: https://orcid.org/0000-0002-1935-279X)
- Jiaqiang Wu (ORCID: https://orcid.org/0009-0002-8289-7853)
- Shuangqin Ran
- Min Lu (ORCID: https://orcid.org/0000-0002-5604-4112)
Institutions
- Shenyang Pharmaceutical University (CN)
- Suzhou Municipal Hospital (CN)
- Nanjing University (CN)
Publication Details
- Journal
- Frontiers in Pediatrics
- Published
- 2025-03-07
- DOI
- https://doi.org/10.3389/fped.2025.1490500
- Citations
- 4
- Primary Topic
- Pneumonia and Respiratory Infections
- Type
- article
- Field-Weighted Citation Impact
- 5.42