A Comprehensive Drift-Adaptive Framework for Sustaining Model Performance in COVID-19 Detection From Dynamic Cough Audio Data: Model Development and Validation.
Journal:
Journal of medical Internet research
Published Date:
Jun 3, 2025
Abstract
BACKGROUND: The COVID-19 pandemic has highlighted the need for robust and adaptable diagnostic tools capable of detecting the disease from diverse and continuously evolving data sources. Machine learning models, particularly convolutional neural networks, are promising in this regard. However, the dynamic nature of real-world data can lead to model drift, where the model's performance degrades over time, as the underlying data distribution changes due to evolving disease characteristics, demographic shifts, and variations in recording conditions. Addressing this challenge is crucial to maintaining the accuracy and reliability of these models in ongoing diagnostic applications.