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

Machine Learning (1984) Clinical Information and Decision Making (1823) Decision Support for Health Professionals (1479) Cardiac Arrhythmia, Atrial Fibrillation (112) Electronic Health Records (1278) Clinical Informatics (1265)

Published on in Vol 9, No 12 (2021): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29225, first published .
Machine Learning Methodologies for Prediction of Rhythm-Control Strategy in Patients Diagnosed With Atrial Fibrillation: Observational, Retrospective, Case-Control Study

Machine Learning Methodologies for Prediction of Rhythm-Control Strategy in Patients Diagnosed With Atrial Fibrillation: Observational, Retrospective, Case-Control Study

Machine Learning Methodologies for Prediction of Rhythm-Control Strategy in Patients Diagnosed With Atrial Fibrillation: Observational, Retrospective, Case-Control Study

Authors of this article:

Rachel S Kim1 Author Orcid Image ;   Steven Simon2 Author Orcid Image ;   Brett Powers1 Author Orcid Image ;   Amneet Sandhu3 Author Orcid Image ;   Jose Sanchez3 Author Orcid Image ;   Ryan T Borne3 Author Orcid Image ;   Alexis Tumolo3 Author Orcid Image ;   Matthew Zipse3 Author Orcid Image ;   J Jason West3 Author Orcid Image ;   Ryan Aleong3 Author Orcid Image ;   Wendy Tzou3 Author Orcid Image ;   Michael A Rosenberg1, 3 Author Orcid Image
Article Authors Cited by (6) Tweetations (3) Metrics

Journals

  1. Simon S, Trinkley K, Malone D, Rosenberg M. Interpretable Machine Learning Prediction of Drug-Induced QT Prolongation: Electronic Health Record Analysis. Journal of Medical Internet Research 2022;24(12):e42163 View
  2. Wegner F, Plagwitz L, Doldi F, Ellermann C, Willy K, Wolfes J, Sandmann S, Varghese J, Eckardt L. Machine learning in the detection and management of atrial fibrillation. Clinical Research in Cardiology 2022;111(9):1010 View
  3. Camm A, Naccarelli G, Mittal S, Crijns H, Hohnloser S, Ma C, Natale A, Turakhia M, Kirchhof P. The Increasing Role of Rhythm Control in Patients With Atrial Fibrillation. Journal of the American College of Cardiology 2022;79(19):1932 View
  4. Barrett C, Suzuki Y, Hussein S, Garg L, Tumolo A, Sandhu A, West J, Zipse M, Aleong R, Varosy P, Tzou W, Banaei‐Kashani F, Rosenberg M. Evaluation of Quantitative Decision‐Making for Rhythm Management of Atrial Fibrillation Using Tabular Q‐Learning. Journal of the American Heart Association 2023;12(9) View
  5. de la Nava A, Ros S, Carta A, González‐Torrecilla E, Mansilla A, Bermejo J, Arenal Á, Climent A, Guillem M, Atienza F. Atrial Fibrillation Treatment Stratification Based on Artificial Intelligence‐Driven Analysis of the Electrophysiological Complexity. Journal of Cardiovascular Electrophysiology 2025;36(8):1903 View
  6. Antoun I, Abdelrazik A, Eldesouky M, Li X, Layton G, Zakkar M, Somani R, Ng G. Artificial intelligence in atrial fibrillation: emerging applications, research directions and ethical considerations. Frontiers in Cardiovascular Medicine 2025;12 View