Stroke prediction in elderly patients with atrial fibrillation using machine learning combined clinical and left atrial appendage imaging phenotypic features.

Journal: BMC cardiovascular disorders

Published Date: May 24, 2025

Abstract

BACKGROUND: Atrial fibrillation (AF) is one of the primary etiologies for ischemic stroke, and it is of paramount importance to delineate the risk phenotypes among elderly AF patients and to investigate more efficacious models for predicting stroke risk.

Authors

Hao Huang

School of Information Science and Engineering, Xinjiang University, Shangli Road, Urumqi 830046, China.
Yan Xiong

School of Chemistry and Chemical Engineering, Southwest Petroleum University, Chengdu 610500, China. xiongyan207@163.com.
Yuan Yao

Department of Food Science, Purdue University, West Lafayette, IN, 47907, USA. Electronic address: yao1@purdue.edu.
Jie Zeng

Department of Experimental Research, Guangxi Medical University Cancer Hospital, Nanning, 530021, China.

Keywords

Age Factors Aged Aged, 80 and over Atrial Appendage Atrial Fibrillation Computed Tomography Angiography Decision Support Techniques Female Humans Incidence Ischemic Stroke Machine Learning Male Phenotype Predictive Value of Tests Prognosis Prospective Studies Risk Assessment Risk Factors Stroke Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (40413386)

Stroke prediction in elderly patients with atrial fibrillation using machine learning combined clinical and left atrial appendage imaging phenotypic features.

Abstract

Authors

Keywords

External Resources

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