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

Machine Learning (3159) Big Data (433) Decision Support for Health Professionals (2222) Advanced Data Analytics in eHealth (350) Cardiac Risk and Cardiac Risk Calculators (169) Cardiac Arrhythmia, Atrial Fibrillation (141)

Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/63795, first published .
ECG monitor in ICU shows patient's vitals: heart rate, brain activity, and respiration.

Using Machine Learning to Predict the Duration of Atrial Fibrillation: Model Development and Validation

Using Machine Learning to Predict the Duration of Atrial Fibrillation: Model Development and Validation

Authors of this article:

Satoshi Shimoo1 Author Orcid Image ;   Keitaro Senoo1, 2 Author Orcid Image ;   Taku Okawa1 Author Orcid Image ;   Kohei Kawai2 Author Orcid Image ;   Masahiro Makino2 Author Orcid Image ;   Jun Munakata2 Author Orcid Image ;   Nobunari Tomura2 Author Orcid Image ;   Hibiki Iwakoshi2 Author Orcid Image ;   Tetsuro Nishimura2 Author Orcid Image ;   Hirokazu Shiraishi1, 2 Author Orcid Image ;   Keiji Inoue3 Author Orcid Image ;   Satoaki Matoba1, 2 Author Orcid Image
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Journals

  1. Osoro L, Arbelo E, Lane D, Mei D, Kozhuharov N, Zylla M, Collins B, Vardas P, Boriani G, Figueras J, Merino J, Pürerfellner H, Burri H, Casado-Arroyo R. From Algorithm Development to Clinical Implementation: A Systematic Review of Artificial Intelligence in Cardiovascular Medicine. Technologies 2026;14(8):511 View