Fair prediction of 2-year stroke risk in patients with atrial fibrillation.

Journal: Journal of the American Medical Informatics Association : JAMIA

PMID: 38960729

Abstract

OBJECTIVE: This study aims to develop machine learning models that provide both accurate and equitable predictions of 2-year stroke risk for patients with atrial fibrillation across diverse racial groups.

Authors

Jifan Gao

Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison.
Philip Mar

Department of Internal Medicine, Saint Louis University, School of Medicine, Saint Louis, MO 63104, United States.
Zheng-Zheng Tang

Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53726, United States.
Guanhua Chen

Vanderbilt University School of Medicine, Nashville, TN.

Keywords

Aged Atrial Fibrillation Black or African American Electronic Health Records Female Humans Machine Learning Male Middle Aged Racial Groups Risk Assessment Risk Factors ROC Curve Stroke

External Resources

View on PubMed Access via DOI PubMed (38960729)

Fair prediction of 2-year stroke risk in patients with atrial fibrillation.

Abstract

Authors

Keywords

External Resources

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