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

Postoperative Monitoring and Telemonitoring (154) Clinical Information and Decision Making (1633)

Published on in Vol 6 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/40402, first published .
Prediction of Pelvic Organ Prolapse Postsurgical Outcome Using Biomaterial-Induced Blood Cytokine Levels: Machine Learning Approach

Prediction of Pelvic Organ Prolapse Postsurgical Outcome Using Biomaterial-Induced Blood Cytokine Levels: Machine Learning Approach

Prediction of Pelvic Organ Prolapse Postsurgical Outcome Using Biomaterial-Induced Blood Cytokine Levels: Machine Learning Approach

Authors of this article:

Mihyun Lim Waugh1 Author Orcid Image ;   Nicholas Boltin1 Author Orcid Image ;   Lauren Wolf1 Author Orcid Image ;   Jane Goodwin2 Author Orcid Image ;   Patti Parker3 Author Orcid Image ;   Ronnie Horner4 Author Orcid Image ;   Matthew Hermes5 Author Orcid Image ;   Thomas Wheeler6 Author Orcid Image ;   Richard Goodwin2 Author Orcid Image ;   Melissa Moss1, 7 Author Orcid Image
Article Authors Cited by (1) Tweetations (2) Metrics

Journals

  1. Waugh M, Mills T, Boltin N, Wolf L, Parker P, Horner R, Wheeler II T, Goodwin R, Moss M. Predicting Transvaginal Surgical Mesh Exposure Outcomes Using an Integrated Dataset of Blood Cytokine Levels and Medical Record Data: Machine Learning Approach. JMIR Formative Research 2025;9:e59631 View