A machine learning–based risk prediction model for atrial fibrillation in critically ill patients

This study demonstrates the potential of machine learning models in predicting AF development in ICU patients. The compact model, with a satisfactory AUROC, can be a valuable tool for identifying high-risk patients and facilitating timely interventions.

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Publication Details

Journal
Heart Rhythm O2
Published
2025-02-21
DOI
https://doi.org/10.1016/j.hroo.2025.02.008
Citations
6
Primary Topic
Atrial Fibrillation Management and Outcomes
Type
article
Field-Weighted Citation Impact
8.33

Funders

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article

A machine learning–based risk prediction model for atrial fibrillation in critically ill patients

Yaman Jarrar, Emmanuel Otabor, Zaid Al-Fakhouri, Justin Lam et al.
6 citations
Heart Rhythm O2
Atrial Fibrillation Management and Outcomes
8.33
article

A machine learning–based risk prediction model for atrial fibrillation in critically ill patients

Yaman Jarrar, Emmanuel Otabor, Zaid Al-Fakhouri, Justin Lam, Laith Alomari, Jana Alomari
article en
6 citations

Abstract

This study demonstrates the potential of machine learning models in predicting AF development in ICU patients. The compact model, with a satisfactory AUROC, can be a valuable tool for identifying high-risk patients and facilitating timely interventions.

Heart Rhythm O2Vol. 6(5)
Lehigh Valley Hospital-Pocono (US), Jordan University of Science and Technology (JO), Thomas Jefferson University Hospital (US), Lehigh Valley Health Network (US), MetroHealth Medical Center (US), Case Western Reserve University (US)
Thomas Jefferson University
Openalex Percentile: Top 2%
Atrial Fibrillation Management and Outcomes
8.33
DOIOpen Access PDFOpenAlex
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A machine learning–based risk prediction model for atrial fibrillation in critically ill patients — Yaman Jarrar, Emmanuel Otabor, et al. · Heart Rhythm O2 (2025) | TGRS Research Map | TGRS