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

Formative Evaluation of Digital Health Interventions (2804) Clinical Information and Decision Making (1639) Artificial Intelligence (1710) mHealth in a Clinical Setting (675) Machine Learning (1723) Obstetrics, Gynecology, and Reproductive Medicine (277)

Published on in Vol 9 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/59631, first published .
Predicting Transvaginal Surgical Mesh Exposure Outcomes Using an Integrated Dataset of Blood Cytokine Levels and Medical Record Data: Machine Learning Approach

Predicting Transvaginal Surgical Mesh Exposure Outcomes Using an Integrated Dataset of Blood Cytokine Levels and Medical Record Data: Machine Learning Approach

Predicting Transvaginal Surgical Mesh Exposure Outcomes Using an Integrated Dataset of Blood Cytokine Levels and Medical Record Data: Machine Learning Approach

Authors of this article:

Mihyun Lim Waugh1 Author Orcid Image ;   Tyler Mills2 Author Orcid Image ;   Nicholas Boltin1 Author Orcid Image ;   Lauren Wolf1 Author Orcid Image ;   Patti Parker3 Author Orcid Image ;   Ronnie Horner4 Author Orcid Image ;   Thomas L Wheeler II5 Author Orcid Image ;   Richard L Goodwin1 Author Orcid Image ;   Melissa A Moss1 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Mihyun Lim Waugh   1 , PhD ;   Tyler Mills   2 , BS ;   Nicholas Boltin   1 , PhD ;   Lauren Wolf   1 , PhD ;   Patti Parker   3 , BSN ;   Ronnie Horner   4 , PhD ;   Thomas L Wheeler II   5 , MD, MSPH ;   Richard L Goodwin   1 , PhD ;   Melissa A Moss   1 , PhD

1 Department of Biomedical Engineering, University of South Carolina, Columbia, SC, United States

2 University of South Carolina School of Medicine, Columbia, SC, United States

3 Prisma Health, Greenville, SC, United States

4 Department of Health Services Research and Administration, University of Nebraska Medical Center, Omaha, NE, United States

5 Department of Obstetrics and Gynecology, Spartanburg Regional Healthcare, Spartanburg, SC, United States

Corresponding Author:

  • Melissa A Moss, PhD
  • Department of Biomedical Engineering
  • University of South Carolina
  • 301 Main St, Rm 2C02
  • Columbia, SC, 29208-4101
  • United States
  • Phone: 1 8646336181
  • Email: [email protected]
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