Machine learning-based model to predict composite thromboembolic events among Chinese elderly patients with atrial fibrillation

Abstract Objective Accurate prediction of survival prognosis is helpful to guide clinical decision-making. The aim of this study was to develop a model using machine learning techniques to predict the occurrence of composite thromboembolic events (CTEs) in elderly patients with atrial fibrillation(A...

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Detalhes bibliográficos

Principais autores:	Jiefeng Ren, Haijun Wang, Song Lai, Yi Shao, Hebin Che, Zaiyao Xue, Xinlian Qi, Sha Zhang, Jinkun Dai, Sai Wang, Kunlian Li, Wei Gan, Quanjin Si
Formato:	Artigo
Idioma:	Inglês
Publicado em:	BMC 2024-08-01
coleção:	BMC Cardiovascular Disorders
Assuntos:	Elderly patients Atrial fibrillation Machine learning techniques Composite thromboembolic events Early prediction model
Acesso em linha:	https://doi.org/10.1186/s12872-024-04082-9
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