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

Formative Evaluation of Digital Health Interventions (3524) Clinical Information and Decision Making (2113) Machine Learning (2270) JMIR Theme Issue: COVID-19 Special Issue (2569)

Published on in Vol 10 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/76542, first published .
Evaluating Biomedical Feature Fusion on Machine Learning’s Predictability and Interpretability of COVID-19 Severity Types: Model Development, Interpretation, and Validation

Evaluating Biomedical Feature Fusion on Machine Learning’s Predictability and Interpretability of COVID-19 Severity Types: Model Development, Interpretation, and Validation

Evaluating Biomedical Feature Fusion on Machine Learning’s Predictability and Interpretability of COVID-19 Severity Types: Model Development, Interpretation, and Validation

Authors of this article:

Haleigh Noelle West-Page1 Author Orcid Image ;   Kevin McGoff1 Author Orcid Image ;   Harrison Latimer1 Author Orcid Image ;   Isaac Olufadewa2 Author Orcid Image ;   Shi Chen2 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Haleigh Noelle West-Page   1 , MS ;   Kevin McGoff   1 , PhD ;   Harrison Latimer   1 , BS ;   Isaac Olufadewa   2 , MBBS, PhD ;   Shi Chen   2 , PhD

1 Department of Mathematics and Statistics, College of Science, University of North Carolina at Charlotte, Charlotte, NC, United States

2 Department of Epidemiology and Community Health, University of North Carolina at Charlotte, Charlotte, NC, United States

Corresponding Author:

  • Haleigh Noelle West-Page, MS
  • Department of Mathematics and Statistics
  • College of Science, University of North Carolina at Charlotte
  • 9201 University City Boulevard
  • Charlotte, NC 28223
  • United States
  • Phone: 1 980-829-8292
  • Email: hwest10@charlotte.edu