Machine learning algorithms for the early detection of bloodstream infection in children with osteoarticular infections
The Random Forest model proved to be effective in early and timely identification of BSI in children with osteoarticular infections. Its application could aid in clinical decision-making and potentially mitigate the risk associated with delayed or inaccurate blood culture results.
Authors
- Yuwen Liu (ORCID: https://orcid.org/0000-0002-4911-5744)
- Guixin Sun (ORCID: https://orcid.org/0000-0002-0962-8561)
- Pengfei Zheng (ORCID: https://orcid.org/0000-0003-3362-0743)
- Jie Chen (ORCID: https://orcid.org/0000-0002-2797-0061)
- Tao Zhang (ORCID: https://orcid.org/0000-0003-1048-4443)
- Wei Ping Hu (ORCID: https://orcid.org/0000-0003-3635-6335)
- Yuhan Wu (ORCID: https://orcid.org/0009-0009-5358-5579)
Institutions
- Shanghai East Hospital (CN)
- Second Affiliated Hospital of Nanjing Medical University (CN)
- Wuxi People's Hospital (CN)
- Nanjing Medical University (CN)
- Nanjing University (CN)
Publication Details
- Journal
- Frontiers in Pediatrics
- Published
- 2024-12-11
- DOI
- https://doi.org/10.3389/fped.2024.1398713
- Citations
- 3
- Primary Topic
- Streptococcal Infections and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 1.63