Machine learning-based risk prediction model for pertussis in children: a multicenter retrospective study
The pertussis prediction model devised in this study was validated using multicenter data, exhibited high prediction performance, and was successfully implemented online. Future research should broaden the data sources and incorporate dynamic data to enhance the model's accuracy and applicability.
Authors
- Xingping Tao
- Cong-yun Liu
- Li Tan (ORCID: https://orcid.org/0000-0001-5961-0450)
- Yi Feng (ORCID: https://orcid.org/0000-0002-3952-1351)
- Richard Ma (ORCID: https://orcid.org/0000-0001-8088-5686)
- Kai Liu (ORCID: https://orcid.org/0000-0003-2573-1531)
- Wenjuan Chen (ORCID: https://orcid.org/0000-0003-4361-980X)
- Liming Cheng (ORCID: https://orcid.org/0000-0001-6444-5090)
- Juan Xie (ORCID: https://orcid.org/0000-0003-3766-4196)
- Yuan Qiao (ORCID: https://orcid.org/0000-0001-8294-6649)
- Hongyan Zhu (ORCID: https://orcid.org/0000-0001-5284-3075)
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Kunming Medical University (CN)
- Fuwai Yunnan Cardiovascular Hospital (CN)
- Kunming Children's Hospital (CN)
- Baoshan University (CN)
- Sixth Affiliated Hospital of Kunming Medical University (CN)
Publication Details
- Journal
- BMC Infectious Diseases
- Published
- 2025-03-27
- DOI
- https://doi.org/10.1186/s12879-025-10797-7
- Citations
- 3
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 4.08