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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Detalles Bibliográficos
Autores principales: Güldener, Ulrich, Kessler, Thorsten, von Scheidt, Moritz, Hawe, Johann S., Gerhard, Beatrix, Maier, Dieter, Lachmann, Mark, Laugwitz, Karl-Ludwig, Cassese, Salvatore, Schömig, Albert W., Kastrati, Adnan, Schunkert, Heribert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10142067/
https://www.ncbi.nlm.nih.gov/pubmed/37109283
http://dx.doi.org/10.3390/jcm12082941

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10142067/
https://www.ncbi.nlm.nih.gov/pubmed/37109283
http://dx.doi.org/10.3390/jcm12082941

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