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

Cardiac Arrhythmia, Atrial Fibrillation (116) Public Health Informatics (137) Machine Learning (2102) Artificial Intelligence (2169) Novel Sensors and Data Acquisition Methods in Cardiology (231)

Published on in Vol 9 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/77380, first published .
Predicting Atrial Fibrillation Ablation Outcomes: Machine Learning Model Development and Validation Using a Large Administrative Claims Database

Predicting Atrial Fibrillation Ablation Outcomes: Machine Learning Model Development and Validation Using a Large Administrative Claims Database

Predicting Atrial Fibrillation Ablation Outcomes: Machine Learning Model Development and Validation Using a Large Administrative Claims Database

Authors of this article:

Yijun Liu1 Author Orcid Image ;   Mustapha Oloko-Oba2 Author Orcid Image ;   Kathryn A Wood2 Author Orcid Image ;   Michael S Lloyd3 Author Orcid Image ;   Joyce C Ho4 Author Orcid Image ;   Vicki Stover Hertzberg2 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Yijun Liu   1 , BS ;   Mustapha Oloko-Oba   2 , PhD ;   Kathryn A Wood   2 , RN, PhD ;   Michael S Lloyd   3 , MD ;   Joyce C Ho   4 , PhD ;   Vicki Stover Hertzberg   2 , PhD

1 Department of Data and Decision Sciences, Emory University, Atlanta, GA, United States

2 Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, United States

3 Division of Cardiology, Department of Medicine, Emory University, Atlanta, GA, United States

4 Department of Computer Science, Emory University, Atlanta, GA, United States

Corresponding Author:

  • Joyce C Ho, PhD
  • Department of Computer Science
  • Emory University
  • 400 Dowman Drive
  • Atlanta, GA 30322
  • United States
  • Email: joyce.c.ho@emory.edu