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Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death

Circulation Research(2020)10.1161/circresaha.120.317345Source: DataRank Database

Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death is a research paper published in Circulation Research (2020). On theSindex it has a DataRank of 0. It has been cited 54 times.

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Open Access54 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (7 grants)Impact (FWCI 5.27)OA: bronzeTopics (69)IDs (PubMed, PMC, OpenAlex)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (16)

Anojan Selvalingam ORCID,Mahmood Alhusseini,David E. Krummen ORCID,Cesare Corrado ORCID,Firas Abuzaid ORCID

Live enrichment

PubMed:33167779 PMC:PMC7855939 OpenAlex:https://openalex.org/W3105222409

HHS | National Institutes of Health

Grant: F32 HL144101

HHS | National Institutes of Health

Grant: K23 HL145017

HHS | National Institutes of Health

Grant: R21 HL145500

HHS | National Institutes of Health

Grant: R01 HL83359

HHS | NIH | National Heart, Lung, and Blood Institute

Grant: R01 HL149134

NHLBI NIH HHS

Grant: R01 HL122384

NHLBI NIH HHS

Grant: R01 HL083359

FWCI

5.27

Citation Percentile

1.0%

Citation Trend

2020

2021

2022

2023

2024

2025

2026

bronze

journalhttps://www.ahajournals.org/doi/pdf/10.1161/CIRCRESAHA.120.317345

publisherhttps://www.ahajournals.org/doi/pdf/10.1161/CIRCRESAHA.120.317345

publisherhttps://www.ahajournals.org/doi/full/10.1161/CIRCRESAHA.120.317345

journalhttps://doi.org/10.1161/circresaha.120.317345

repositoryhttps://pubmed.ncbi.nlm.nih.gov/33167779

Fields of Study

Cardiac electrophysiology and arrhythmiasCardiac pacing and defibrillation studiesECG Monitoring and AnalysisAction PotentialsAgedAged, 80 and overCardiomyopathiesDeath, Sudden, CardiacDiagnosis, Computer-AssistedElectrophysiologic Techniques, CardiacFemaleHumansMaleMiddle AgedMyocardial InfarctionNeural Networks, ComputerPhenotypePredictive Value of TestsPrognosisProspective StudiesRisk AssessmentRisk FactorsSignal Processing, Computer-AssistedSupport Vector MachineTachycardia, VentricularTime FactorsVentricular Fibrillation

MeSH Terms

Action PotentialsAgedAged, 80 and overDiagnosis, Computer-AssistedFemaleHumansMaleMiddle AgedCardiomyopathiesMyocardial InfarctionPhenotypePredictive Value of TestsPrognosisProspective StudiesRisk FactorsSignal Processing, Computer-AssistedTime FactorsVentricular FibrillationNeural Networks, ComputerDeath, Sudden, CardiacTachycardia, VentricularRisk AssessmentElectrophysiologic Techniques, CardiacSupport Vector Machine

Keywords

CardiologyVentricular tachycardiaIschemic cardiomyopathyInternal medicineMedicineVentricular fibrillationCardiomyopathyEjection fractionCoronary artery diseaseSudden cardiac deathMachine learningHeart failureComputer scienceIon channelsArtificial intelligenceCoronary diseasesystems biologyDeath, Sudden, Cardiac

Sustainable Development Goals

SDG 0: Good health and well-being