Treatment of individual predictors with neural network algorithms improves Global Registry of Acute Coronary Events score discrimination.

Journal: Archivos de cardiologia de Mexico

PMID: 33661883

Abstract

OBJECTIVE: The aim of this study was to develop, train, and test different neural network (NN) algorithm-based models to improve the Global Registry of Acute Coronary Events (GRACE) score performance to predict in-hospital mortality after an acute coronary syndrome.

Authors

Raul A Borracci

Biostatistics, School of Medicine, Austral University.
Claudio C Higa

Department of Cardiology, Herzzentrum, Hospital Alemán. Buenos Aires, Argentina.
Graciana Ciambrone

Department of Cardiology, Herzzentrum, Hospital Alemán. Buenos Aires, Argentina.
Jimena Gambarte

Department of Cardiology, Herzzentrum, Hospital Alemán. Buenos Aires, Argentina.

Keywords

Acute Coronary Syndrome Aged Algorithms Databases, Factual Female Hospital Mortality Humans Male Middle Aged Neural Networks, Computer Prognosis Registries

External Resources

View on PubMed Access via DOI PubMed (33661883)

Treatment of individual predictors with neural network algorithms improves Global Registry of Acute Coronary Events score discrimination.

Abstract

Authors

Keywords

External Resources

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