Determining optimal strategies for personalized atrial fibrillation treatment in intensive care unit patients using a deep learning-based causal inference approach: rhythm and/or rate control

Using a deep learning-based causal inference model, we quantified mortality reduction for each treatment strategy and identified the patient characteristics associated with the most favorable outcomes for each strategy.

Authors

Yoonjin Kang (ORCID: https://orcid.org/0000-0002-2528-173X)
Min Woo Kang (ORCID: https://orcid.org/0000-0002-9411-3481)
Shin Young Ahn (ORCID: https://orcid.org/0000-0002-6844-0614)

Institutions

Korea University Medical Center (KR)
New Generation University College (ET)
Seoul National University Hospital (KR)

Publication Details

Journal: Journal of the American Medical Informatics Association
Published: 2025-11-12
DOI: https://doi.org/10.1093/jamia/ocaf203
Primary Topic: Advanced Causal Inference Techniques
Type: article
Field-Weighted Citation Impact: 0.00

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article

Determining optimal strategies for personalized atrial fibrillation treatment in intensive care unit patients using a deep learning-based causal inference approach: rhythm and/or rate control

Yoonjin Kang, Min Woo Kang, Shin Young Ahn

Nov 12, 2025

Journal of the American Medical Informatics Association

Advanced Causal Inference Techniques

article

Determining optimal strategies for personalized atrial fibrillation treatment in intensive care unit patients using a deep learning-based causal inference approach: rhythm and/or rate control

Yoonjin Kang, Min Woo Kang, Shin Young Ahn

November 12, 2025article en

Abstract

Using a deep learning-based causal inference model, we quantified mortality reduction for each treatment strategy and identified the patient characteristics associated with the most favorable outcomes for each strategy.

Journal of the American Medical Informatics AssociationVol. 33(3)

Korea University Medical Center (KR), New Generation University College (ET), Seoul National University Hospital (KR)

Openalex Percentile: Top 66%

Advanced Causal Inference Techniques

DOI Source OpenAlex

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