Development and validation of machine learning-based in-hospital mortality predictive models for acute aortic syndrome in emergency departments

Key Points

• The predictive models for in-hospital mortality showed noteworthy performance, enhancing clinical decision-making.
• Model performance metrics suggest reliable predictions for acute aortic syndrome burden during hospital stays.
• Analysis utilized machine learning tools to construct models, improving accuracy for emergency department assessments.
• The findings support the importance of integrating machine learning applications in critical care settings.