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Interpretable machine learning-based predictive modeling of patient outcomes following cardiac surgery

Journal of Thoracic and Cardiovascular Surgery(2023)10.1016/j.jtcvs.2023.11.034Source: DataRank Database

Interpretable machine learning-based predictive modeling of patient outcomes following cardiac surgery is a research paper published in Journal of Thoracic and Cardiovascular Surgery (2023). On theSindex it has a DataRank of 0. It has been cited 18 times.

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Open Access18 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (11 grants)OA: greenTopics (24)IDs (PubMed, PMC)

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Authors (9)

Cindy Li,Max Dekle,C. Bermúdez ORCID,Daniel Brodie ORCID,Frank W. Sellke ORCID

Live enrichment

PubMed:38040328 PMC:PMC11133766

National Heart, Lung, and Blood Institute

Grant: T32 HL 134625

NIGMS

Grant: P20 GM 103652

NIGMS

Grant: U54 GM 115677

NHLBI NIH HHS

Grant: R25 HL088992

NIGMS NIH HHS

Grant: P30 GM149398

NHLBI NIH HHS

Grant: L30 HL149074

Extracorporeal Life Support Organization

NIH

Francis Family Foundation

LivaNova

Merck

greenLicense: http://www.elsevier.com/open-access/userlicense/1.0/

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC11133766/pdf/nihms-1949309.pdf

publisherhttps://api.elsevier.com/content/article/PII:S0022522323011108?httpAccept=text/xml

publisherhttps://api.elsevier.com/content/article/PII:S0022522323011108?httpAccept=text/plain

MeSH Terms

HumansPostoperative HemorrhagePostoperative ComplicationsTreatment OutcomeCardiac Surgical ProceduresRisk AssessmentRisk FactorsPredictive Value of TestsDecision Support TechniquesDatabases, FactualAgedMiddle AgedFemaleMaleStrokeVenous ThromboembolismMachine Learning

Keywords

StrokeVenous ThromboembolismPatient OutcomesAdult Cardiac SurgeryExplainable Machine LearningSociety Of Thoracic Surgeons Adult Cardiac Surgery DatabaseHemorrhage Requiring Reoperation