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A machine learning-based phenotype for long COVID in children: An EHR-based study from the RECOVER program

PLoS ONE(2023)10.1371/journal.pone.0289774Source: DataRank Database

A machine learning-based phenotype for long COVID in children: An EHR-based study from the RECOVER program is a research paper published in PLoS ONE (2023). On theSindex it has a DataRank of 0. It has been cited 18 times.

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0DataRank · unranked

Scored on demand from live citation data

Open Access18 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (2 grants)OA: goldTopics (20)IDs (PubMed)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 (18)

Hanieh Razzaghi ORCID,Xing Song ORCID,Keith Morse ORCID,Levon Utidjian ORCID,Andrea J. Allen ORCID

Live enrichment

PubMed:19965766

National Institute of Health

Grant: OT2HL161847-01

National Institutes of Health

Grant: 3OT2HL161847-01S3

OTA-21-015A Post-Acute Sequelae of SARS-CoV-2 Infection Initiative: NYU Langone Health Clinical Science Core, Data Resource Core, and PASC Biorepository Core

goldLicense: cc-by

publisherhttps://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0289774&type=printable

publisherhttps://dx.plos.org/10.1371/journal.pone.0289774

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC10414557/pdf/pone.0289774.pdf

repositoryhttps://doaj.org/article/3a2ab00d502b44c08376bda8b801ef27

https://doi.org/10.1101/2022.12.22.22283791

Fields of Study

03 medical and health sciences0302 clinical medicine

Keywords

Long COVIDSARS-CoV-2ScienceQRCOVID-19ArticleSystemic Inflammatory Response SyndromeMachine LearningSARS-CoV2 infectionPost-Acute COVID-19 SyndromePhenotypeDisease ProgressionMedicineHumansPost-Acute SequelaeChildResearch Article

Sustainable Development Goals

SDG 3: 3. Good health