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Article

Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death.

Circulation research - 22 Jan 2021

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

Abstract excerpt

RATIONALE: Susceptibility to VT/VF (ventricular tachycardia/fibrillation) 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 cardiomyopathy, by training then interpreting machine learning of ventricular monophasic action potentials (MAPs)...

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Share to Death, Sudden, Cardiac
Share to Diagnosis, Computer-Assisted

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Electrophysiologic Techniques, Cardiac

Share to Neural Networks, Computer

Share to Signal Processing, Computer-Assisted

Share to Support Vector Machine

Share to Tachycardia, Ventricular

Share to Ventricular Fibrillation

Source

Open in PubMed Open full text

Identifiers

PMID: 33167779
DOI: 10.1161/CIRCRESAHA.120.317345
PMCID: PMC7855939

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026. Manifest publication-graph-cohort-2026-09-06.1.

Source receipt: pubmed, retrieved 6 Sept 2026.

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