This paper is in the following e-collection/theme issue:

Artificial Intelligence (2159) JMIR Theme Issue: COVID-19 Special Issue (2555) Machine Learning (2091) Clinical Information and Decision Making (1932)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66919, first published .
A Comprehensive Drift-Adaptive Framework for Sustaining Model Performance in COVID-19 Detection From Dynamic Cough Audio Data: Model Development and Validation

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

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

Authors of this article:

Theofanis Ganitidis1 Author Orcid Image ;   Maria Athanasiou1 Author Orcid Image ;   Konstantinos Mitsis1 Author Orcid Image ;   Konstantia Zarkogianni2 Author Orcid Image ;   Konstantina S Nikita1, 3 Author Orcid Image
Article Authors Cited by (1) Tweetations (1) Metrics

Theofanis Ganitidis   1 , MEng ;   Maria Athanasiou   1 , MEng, PhD ;   Konstantinos Mitsis   1 , MEng, PhD ;   Konstantia Zarkogianni   2 , MEng, PhD ;   Konstantina S Nikita   1, 3 , MEng, MD, PhD

1 School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece

2 Faculty of Science and Engineering, Maastricht University, Maastricht, The Netherlands

3 Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States

Corresponding Author:

  • Theofanis Ganitidis, MEng
  • School of Electrical and Computer Engineering
  • National Technical University of Athens
  • 9, Iroon Polytechniou St
  • Athens 15772
  • Greece
  • Phone: 30 2107722285
  • Email: theogani@biosim.ntua.gr