Machine Learned Cellular Phenotypes Predict Outcome in Ischemic Cardiomyopathy

RATIONALE: Susceptibility to ventricular arrhythmias (VT/VF) is difficult to predict in patients with ischemic cardiomyopathy either by clinical tools or by attempting to translate cellular mechanisms to the bedside. OBJECTIVE: To develop computational phenotypes of patients with ischemic cardiomyop...

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Detaylı Bibliyografya
Yayımlandı:Circ Res
Asıl Yazarlar: Rogers, Albert J., Selvalingam, Anojan, Alhusseini, Mahmood I., Krummen, David E., Corrado, Cesare, Abuzaid, Firas, Baykaner, Tina, Meyer, Christian, Clopton, Paul, Giles, Wayne, Bailis, Peter, Niederer, Steven, Wang, Paul J., Rappel, Wouter-Jan, Zaharia, Matei, Narayan, Sanjiv M.
Materyal Türü: Artigo
Dil:Inglês
Baskı/Yayın Bilgisi: 2020
Konular:
Article
Online Erişim:https://ncbi.nlm.nih.gov/pmc/articles/PMC7855939/
https://ncbi.nlm.nih.gov/pubmed/33167779
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1161/CIRCRESAHA.120.317345
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