Interpretable machine learning-derived nomogram model for early detection of persistent diarrhea in Salmonella typhimurium enteritis: a propensity score matching based case-control study
The developed nomogram models provide a practical tool for the early identification of children at high risk of persistent diarrhea, facilitating intervention, potentially preventing serious sequelae, and improving the prognosis of children with S. typhimurium enteritis.
Authors
- Haojie Jin (ORCID: https://orcid.org/0000-0001-9295-1951)
- Yucheng Huang (ORCID: https://orcid.org/0009-0002-9116-6278)
- Longteng Jin
- Maoping Chu (ORCID: https://orcid.org/0000-0003-0975-7176)
- Lu Zhan (ORCID: https://orcid.org/0000-0003-1867-0304)
- Yiping Chen (ORCID: https://orcid.org/0000-0002-8399-763X)
- Jianan Xi
Institutions
- Wenzhou Medical University (CN)
- Second Affiliated Hospital & Yuying Children's Hospital of Wenzhou Medical University (CN)
- Hangzhou Children's Hospital (CN)
- University of Pennsylvania (US)
Publication Details
- Journal
- BMC Infectious Diseases
- Published
- 2025-02-10
- DOI
- https://doi.org/10.1186/s12879-025-10587-1
- Primary Topic
- Salmonella and Campylobacter epidemiology
- Type
- article
- Field-Weighted Citation Impact
- 0.00