Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Sep 27, 2024
Date Accepted: Apr 13, 2025

The final, peer-reviewed published version of this preprint can be found here:

A Comprehensive Drift-Adaptive Framework for Sustaining Model Performance in COVID-19 Detection From Dynamic Cough Audio Data: Model Development and Validation

Ganitidis T, Athanasiou M, Mitsis K, Zarkogianni K, Nikita KS

A Comprehensive Drift-Adaptive Framework for Sustaining Model Performance in COVID-19 Detection From Dynamic Cough Audio Data: Model Development and Validation

J Med Internet Res 2025;27:e66919

DOI: 10.2196/66919

PMID: 40459919

PMCID: 12174887

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Per the author's request this version is not available.