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September 25, 2025Open Access

Predicting ICU Transfer and Short-term Mortality in Emergency Department Atrial Fibrillation Patients: An Enhanced Machine Learning Model Using MIMIC Data

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Authors

MPMaryam PishgarTLTao LiZLZhuoyang Li

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Overview

Machine learning predicts ICU transfer and short-term mortality in atrial fibrillation patients, indicating the importance of timely interventions.

Key Points

  • The machine learning model achieved an AUROC of 0.7979 for predicting ICU transfer in atrial fibrillation patients.
  • LightGBM showed the most effective results for predicting 7- and 30-day mortality with AUROCs of 0.8316 and 0.8010, respectively.
  • The combined cohort was constructed from MIMIC-IV databases, utilizing clinical variables assessed at triage to enhance predictive accuracy.
  • Utilizing feature selection techniques, the model identified 19 key predictors, emphasizing the importance of O2sat, acuity, and resprate.

Cite This Study

Pishgar et al. (2025) studied this question.

synapsesocial.com/papers/68d5dabfddad3c16d463701chttps://doi.org/10.1101/2025.09.22.25336382
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Developing an ICU Mortality Risk Prediction Model for Acute Myocardial Infarction Patients Based on Machine Learning2025
  2. 2Machine learning model for predicting in-hospital cardiac mortality among atrial fibrillation patients2025
  3. 3Explainable machine learning model for early prediction of ICU death in chronic heart failure with pulmonary infection2026
  4. 4Predicting in-hospital mortality in ICU patients with Coronary heart disease and diabetes mellitus using machine learning models.2025 · 4 citations
  5. 5Development and External Validation of a High-Precision Model for Predicting ICU Admission from Emergency Department Triage2025