Reducing invasive RSV diagnostic testing with machine learning: A retrospective validation study
This study demonstrates that ML-based screening using symptom data could substantially reduce unnecessary invasive RSV testing while maintaining high diagnostic accuracy. The approach offers promising clinical utility by potentially minimizing patient discomfort and optimizing resource allocation in pediatric respiratory care.
Authors
- Shota Kawamoto (ORCID: https://orcid.org/0000-0001-7699-6375)
- Yoshihiko Morikawa (ORCID: https://orcid.org/0000-0002-1437-0374)
- Naohisa Yahagi (ORCID: https://orcid.org/0000-0002-8083-6072)
Institutions
- Keio University Shonan Fujisawa (JP)
Publication Details
- Journal
- Journal of Infection and Public Health
- Published
- 2025-09-04
- DOI
- https://doi.org/10.1016/j.jiph.2025.102967
- Citations
- 2
- Primary Topic
- Respiratory viral infections research
- Type
- article
- Field-Weighted Citation Impact
- 2.71