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Fairness-Optimized Synthetic EHR Generation for Arbitrary Downstream Predictive Tasks

(2025)10.1145/3721201.3721373Source: DataRank Database

Fairness-Optimized Synthetic EHR Generation for Arbitrary Downstream Predictive Tasks is a research paper (2025). On theSindex it has a DataRank of 0.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access0 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (5 grants)Impact (FWCI 0.00)OA: goldTopics (16)IDs (PubMed, OpenAlex)

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 (3)

Raphael Poulain ORCID,Rahmatollah Beheshti ORCID,Mirza Farhan Bin Tarek ORCID

Live enrichment

PubMed:19162232 OpenAlex:https://openalex.org/W4416144389

NIH (National Institutes of Health)

Grant: P20GM103446

NIH (National Institutes of Health)

Grant: P20GM113125

NIH (National Institutes of Health)

Grant: U54-GM104941

National Institutes of Health

Grant: 1P20GM113125-01

Center of Biomedical Research Excellence in Cardiovascular Health

National Institutes of Health

Grant: 5P20GM103446-15

Bioinformatics Core

FWCI

0.00

Citation Percentile

0.3%

goldLicense: cc-by

https://dl.acm.org/doi/pdf/10.1145/3721201.3721373

https://doi.org/10.1145/3721201.3721373

https://dx.doi.org/10.48550/arxiv.2406.02510

http://arxiv.org/abs/2406.02510

Fields of Study

Machine Learning in HealthcareElectronic Health Records SystemsArtificial Intelligence in Healthcare and Education0202 electrical engineering, electronic engineering, information engineering02 engineering and technology

Keywords

ToolboxDownstream (manufacturing)Pipeline (software)Key (lock)Focus (optics)WorkflowHealth careReal world dataMachine LearningFOS: Computer and information sciencesMachine Learning (cs.LG)