Evaluation of a Machine Learning Model Based on Laboratory Parameters for the Prediction of Influenza A and B in Chongqing, China: Multicenter Model Development and Validation Study
The CATB-based AI-Lab tool demonstrated high diagnostic accuracy for influenza A and B subtyping using routine laboratory data, achieving performance comparable to rapid antigen testing. By enabling timely subtype differentiation without specialized equipment, this system addresses critical gaps in managing influenza outbreaks, particularly in resource-constrained regions.
Authors
- Chunyan Yang
- Yong Chen (ORCID: https://orcid.org/0000-0003-3673-8667)
- Shi Shan
- Jian Dong
- Xingwang Peng
- W. Hu (ORCID: https://orcid.org/0000-0003-2008-6975)
- Jin Li (ORCID: https://orcid.org/0000-0002-2325-3057)
- Honglin Wang
- Yulong Liu (ORCID: https://orcid.org/0009-0001-0898-9234)
Institutions
- The Affiliated Yongchuan Hospital of Chongqing Medical University (CN)
- Chongqing Dazu District People's Hospital (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-05-15
- DOI
- https://doi.org/10.2196/67847
- Citations
- 4
- Primary Topic
- Influenza Virus Research Studies
- Type
- article
- Field-Weighted Citation Impact
- 5.42