Machine Learning Identifies New Predictors on Restenosis Risk after Coronary Artery Stenting in 10,004 Patients with Surveillance Angiography

Machine Learning Identifies New Predictors on Restenosis Risk after Coronary Artery Stenting in 10,004 Patients with Surveillance Angiography

Objective: Machine learning (ML) approaches have the potential to uncover regular patterns in multi-layered data. Here we applied self-organizing maps (SOMs) to detect such patterns with the aim to better predict in-stent restenosis (ISR) at surveillance angiography 6 to 8 months after percutaneous...

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Bibliographic Details
Main Authors: Ulrich Güldener, Thorsten Kessler, Moritz von Scheidt, Johann S. Hawe, Beatrix Gerhard, Dieter Maier, Mark Lachmann, Karl-Ludwig Laugwitz, Salvatore Cassese, Albert W. Schömig, Adnan Kastrati, Heribert Schunkert
Format: Article
Language:English
Published: MDPI AG 2023-04-01
Series:Journal of Clinical Medicine
Subjects:
artificial intelligence
coronary artery disease
machine learning
percutaneous coronary intervention
prediction
restenosis
Online Access:https://www.mdpi.com/2077-0383/12/8/2941

Internet

https://www.mdpi.com/2077-0383/12/8/2941

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