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Titel:

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

Dokumenttyp:
Journal Article
Autor(en):
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
Abstract:
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 coronary intervention with stenting. METHODS: In prospectively collected data from 10,004 patients receiving percutaneous coronary intervention (PCI) for 15,004 lesions, we applied SOMs to predict ISR...     »
Zeitschriftentitel:
J Clin Med
Jahr:
2023
Band / Volume:
12
Heft / Issue:
8
Volltext / DOI:
doi:10.3390/jcm12082941
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/37109283
TUM Einrichtung:
Klinik für Herz- und Kreislauferkrankungen im Erwachsenenalter (DHM) (Prof. Schunkert); Klinik und Poliklinik für Innere Medizin I, Kardiologie (Prof. Laugwitz)
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