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Synthetic Data Distillation Enables the Extraction of Clinical Information at Scale

medRxiv(2024)10.1101/2024.09.27.24314517Source: DataRank Database

Synthetic Data Distillation Enables the Extraction of Clinical Information at Scale is a research paper published in medRxiv (2024). On theSindex it has a DataRank of 0. It has been cited 2 times.

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Open Access2 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (2 grants)OA: greenTopics (22)IDs (PubMed, OpenAlex)

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Open Access

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Authors (4)

Michael C. Burkhart ORCID,Emily Alsentzer ORCID,Brett K. Beaulieu‐Jones ORCID,Elizabeth Geena Woo ORCID

Live enrichment

PubMed:21097893 OpenAlex:https://openalex.org/W4402944176

National Institutes of Health

Grant: 2UL1TR000430-06

Re-Engineering Translational Research at the University of Chicago

National Institutes of Health

Grant: 5R00NS114850-05

Characterizing Population Differences between Clinical Trial and Real World Populations

greenLicense: cc-by

repositoryhttps://www.medrxiv.org/content/medrxiv/early/2024/09/28/2024.09.27.24314517.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2024.09.27.24314517

repositoryhttps://doi.org/10.1101/2024.09.27.24314517

https://doi.org/10.1038/s41746-025-01681-4

Fields of Study

Machine Learning and Data ClassificationSemantic Web and OntologiesScientific Computing and Data Management0202 electrical engineering, electronic engineering, information engineering02 engineering and technology

Keywords

DistillationExtraction (chemistry)Scale (ratio)Computer scienceProcess engineeringInformation extractionChromatographyData scienceData miningInformation retrievalChemistryEngineeringGeographyCartographyComputer applications to medicine. Medical informaticsR858-859.7Article