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Machine learning using institution-specific multi-modal electronic health records improves mortality risk prediction for cardiac surgery patients

JTCVS Open(2023)10.1016/j.xjon.2023.03.010Source: DataRank Database

Machine learning using institution-specific multi-modal electronic health records improves mortality risk prediction for cardiac surgery patients is a research paper published in JTCVS Open (2023). On theSindex it has a DataRank of 0. It has been cited 16 times.

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Open Access16 citations

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Enrichment:Funding (6 grants)OA: diamondTopics (9)IDs (PubMed, PMC)Annotations (1)

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Findable (1/2)

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Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (8)

Arjun S. Yadaw ORCID,David L. Meretzky,Matthew A. Levin ORCID,David Adams ORCID,Ken McCardle ORCID

Live enrichment

PubMed:37425442 PMC:PMC10328834

National Institutes of Health

Grant: P50GM071558

NHGRI NIH HHS

Grant: R01 HG011407

National Institutes of Health

Grant: 5P50GM071558-04

PROJECT III

National Institutes of Health

Grant: 1F32GM013039-01

MOLECULAR ANALYSIS OF THE YEAST HEAT SHOCK TRANSCRIPTION

National Institutes of Health

Grant: 5R01HG011407-03

Integrating genomic and clinical data to predict disease phenotypes using heterogeneous ensembles

Medtronic

diamondLicense: CC BY NC ND

publisherhttps://api.elsevier.com/content/article/PII:S2666273623000852?httpAccept=text/xml

publisherhttps://api.elsevier.com/content/article/PII:S2666273623000852?httpAccept=text/plain

Europe_PMChttps://europepmc.org/articles/PMC10328834

Europe_PMChttps://europepmc.org/articles/PMC10328834?pdf=render

https://doi.org/10.1016/j.xjon.2023.03.010

Fields of Study

03 medical and health sciences0302 clinical medicine

Keywords

Cardiac surgeryClinical OutcomesMachine LearningRisk PredictionElectronic Health RecordsPrediction ModelingAdult: Risk Scores: Evolving Technology

Machine learning using institution-specific multi-modal electronic health records improves mortality risk prediction for cardiac surgery patients

FAIR Checklist

Authors (8)

Funding (6 grants)

Open Access

Topics

Annotations (Europe PMC)