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

Y.-H. Fu; Yilan Yang; Hua Zhang; Daidai Wang; Qiangrong Zhai; Lanfang Du; Muyesai Nijiati; Yanxia Gao; Qingbian Ma

doi:10.5847/wjem.j.1920-8642.2026.022

January 1, 2026World Journal of Emergency MedicineOpen Access

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

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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.

Implication

Observational analysis develops predictive models for in-hospital mortality in acute aortic syndrome, indicating enhanced decision-making in emergency care.

Abstract

Both kinds of models were built based on machine learning tools, and proved to have certain prediction performance and extrapolation.

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Cite This Study

Fu et al. (2026) studied this question.

synapsesocial.com/papers/69a75d1ac6e9836116a26957 https://doi.org/10.5847/wjem.j.1920-8642.2026.022

View Full Paper

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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