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

Foundations of AI (47) Applications of AI (104)

Published on in Vol 3 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48067, first published .
Improving Risk Prediction of Methicillin-Resistant Staphylococcus aureus Using Machine Learning Methods With Network Features: Retrospective Development Study

Improving Risk Prediction of Methicillin-Resistant Staphylococcus aureus Using Machine Learning Methods With Network Features: Retrospective Development Study

Improving Risk Prediction of Methicillin-Resistant Staphylococcus aureus Using Machine Learning Methods With Network Features: Retrospective Development Study

Authors of this article:

Methun Kamruzzaman1 Author Orcid Image ;   Jack Heavey1 Author Orcid Image ;   Alexander Song1 Author Orcid Image ;   Matthew Bielskas1 Author Orcid Image ;   Parantapa Bhattacharya1 Author Orcid Image ;   Gregory Madden2 Author Orcid Image ;   Eili Klein3, 4 Author Orcid Image ;   Xinwei Deng5 Author Orcid Image ;   Anil Vullikanti1, 6 Author Orcid Image
Article Authors Cited by Tweetations (3) Metrics

Methun Kamruzzaman   1 * , PhD ;   Jack Heavey   1 * , BS ;   Alexander Song   1 * ;   Matthew Bielskas   1 * , MSc ;   Parantapa Bhattacharya   1 * , PhD ;   Gregory Madden   2 * , MD ;   Eili Klein   3, 4 * , PhD ;   Xinwei Deng   5 * , PhD ;   Anil Vullikanti   1, 6 * , PhD

1 University of Virginia, Charlottesville, VA, United States

2 Division of Infectious Diseases & International Health, Department of Medicine, University of Virginia School of Medicine, Charlottesville, VA, United States

3 Department of Emergency Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States

4 Center for Disease Dynamics, Economics and Policy, Washington, DC, DC, United States

5 Department of Statistics, Virginia Tech, Blacksburg, VA, United States

6 Department of Computer Science, University of Virginia, Charlottesville, VA, United States

*all authors contributed equally

Corresponding Author:

  • Anil Vullikanti, PhD
  • University of Virginia
  • Biocomplexity Institute P.O. Box 400298
  • Charlottesville, VA, 22904
  • United States
  • Phone: 1 5405773102
  • Email: vsakumar@virginia.edu