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August 14, 2025Scientific ReportsOpen Access

Machine learning model for predicting in-hospital cardiac mortality among atrial fibrillation patients

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Authors

HLHuasheng LvXBXuehua BiSSShuai Shang

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Overview

This model predicts in-hospital cardiac mortality in atrial fibrillation patients, highlighting key predictors.

Key Points

  • XGBoost model predicts cardiac mortality with high accuracy, showing area under the curve of 0.964 in training.
  • Validation results demonstrate robust performance, achieving an area under the curve of 0.932 in a separate set of patients.
  • The model utilizes 79 clinical variables from electronic medical records to deliver individualized risk assessments.
  • Further multi-center validation is essential to enhance the model's reliability and broader application in clinical settings.

Cite This Study

Lv et al. (2025) studied this question.

synapsesocial.com/papers/68a34f4c234c60ad5c20b6f9https://doi.org/10.1038/s41598-025-14579-8
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Also Consider

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

  1. 1Machine learning‐based prediction of atrial fibrillation in patients with atrial high‐rate episodes2025 · 3 citations
  2. 2Predicting ICU Transfer and Short-term Mortality in Emergency Department Atrial Fibrillation Patients: An Enhanced Machine Learning Model Using MIMIC Data2025
  3. 3Machine learning-based prediction model for recurrence after radiofrequency catheter ablation in patients with atrial fibrillation2025 · 7 citations
  4. 4Optimizing clinical prediction model for new-onset atrial fibrillation in critically ill patient: Based on machine learning2025
  5. 5Predicting Atrial Fibrillation Ablation Outcomes: Machine Learning Model Development and Validation Using a Large Administrative Claims Database2025