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

Formative Evaluation of Digital Health Interventions (3527) Clinical Information and Decision Making (2120) Obstetrics, Gynecology, and Reproductive Medicine (351) mHealth in a Clinical Setting (789) Machine Learning (2273) Artificial Intelligence (2416)

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
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Conference Proceedings

  1. Karki R, Goel S, Kumari S, Bhatnagar R, Kumar R. 2026 5th International Conference on Innovative Practices in Technology and Management (ICIPTM). INNOVITALS- a Healthcare Application Using Random Forest Algorithm View