A Comprehensive Drift-Adaptive Framework for Sustaining Model Performance in COVID-19 Detection From Dynamic Cough Audio Data: Model Development and Validation
The proposed framework successfully addresses the challenge of model drift in COVID-19 detection by enabling continuous adaptation to evolving data distributions. This approach ensures sustained model performance over time, contributing to the development of robust and adaptable diagnostic tools for COVID-19 and potentially other infectious diseases.
Authors
- Μαρία Αθανασίου (ORCID: https://orcid.org/0000-0003-1575-9100)
- Theofanis Ganitidis (ORCID: https://orcid.org/0009-0006-7794-9793)
- Konstantinos Mitsis (ORCID: https://orcid.org/0000-0002-4629-2163)
- Konstantia Zarkogianni (ORCID: https://orcid.org/0000-0003-3886-1618)
- Konstantina S. Nikita (ORCID: https://orcid.org/0000-0001-8255-4354)
Institutions
- University of Southern California (US)
- National Technical University of Athens (GR)
- Maastricht University (NL)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-06-03
- DOI
- https://doi.org/10.2196/66919
- Citations
- 2
- Primary Topic
- Music and Audio Processing
- Type
- article
- Field-Weighted Citation Impact
- 2.97
Funders
- Hellenic Foundation for Research and Innovation