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Early prediction of level-of-care requirements in patients with COVID-19

eLife(2020)10.7554/elife.60519Source: DataRank Database

Early prediction of level-of-care requirements in patients with COVID-19 is a research paper published in eLife (2020). On theSindex it has a DataRank of 0. It has been cited 68 times.

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Open Access68 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (8 grants)Impact (FWCI 6.85)OA: goldTopics (77)Linked Datasets (2)DataCite Reuse (2)IDs (PubMed, PMC, OpenAlex)Annotations (1)SDGs (4)

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Findable (2/2)

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Accessible (1/2)

Open Access

Interoperable (2/2)

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Reusable (0/3)

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Authors (9)

Shahabeddin Sotudian ORCID,Taiyao Wang ORCID,Tingting Xu ORCID,Yang Hu ORCID,Apostolos Gaitanidis ORCID

Live enrichment

PubMed:33044170 PMC:PMC7595731 OpenAlex:https://openalex.org/W3092208592

Office of Naval Research

Grant: N00014-19-1-2571

National Science Foundation

Grant: IIS-1914792

NIGMS NIH HHS

Grant: R01 GM135930

National Institutes of Health

Grant: UL54 TR004130

National Science Foundation

Grant: CNS-1645681

National Science Foundation

Grant: DMS-1664644

National Science Foundation

Grant: 1914792

SCH: INT: Distributed Analytics for Enhancing Fertility in Families

National Institutes of Health

Grant: 1R01GM135930-01

Optimizing and Learning Strategies for Protein Docking

FWCI

6.85

Citation Percentile

1.0%

Citation Trend

2020

2021

2022

2023

2024

2025

goldLicense: cc-by

journalhttps://doi.org/10.7554/elife.60519

publisherhttps://doi.org/10.7554/elife.60519

repositoryhttps://pubmed.ncbi.nlm.nih.gov/33044170

repositoryhttps://doaj.org/article/29e3eae1bf974ebbb0ee81ea472657a3

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/7595731

Fields of Study

Sepsis Diagnosis and TreatmentCOVID-19 Clinical Research StudiesEmergency and Acute Care Studies03 medical and health sciences0302 clinical medicine

MeSH Terms

BetacoronavirusProcedures and Techniques UtilizationCOVID-19SARS-CoV-2AdultAgedDiabetes MellitusFemaleHealth Services Needs and DemandHospitalizationHumansIntensive Care UnitsMaleMassachusettsMiddle AgedPneumonia, ViralRespiration, ArtificialVentilators, MechanicalRisk FactorsROC CurveComorbidityBody Mass IndexNonlinear DynamicsCoronavirus InfectionsArea Under CurvePandemics

Keywords

MedicineMechanical ventilationPneumoniaCoronavirus disease 2019 (COVID-19)Emergency medicineIntensive care medicineVentilation (architecture)Vital signsSeverity of illnessDiabetes mellitusDiseaseInternal medicineSurgeryInfectious disease (medical specialty)HumanArtificial intelligenceMicrobiologyInfectious diseaseCritical CareMachine LearningRisk PredictionCovid-19Sars-cov-2AdultMaleQH301-705.5SciencePneumonia, ViralComorbidityBody Mass IndexBetacoronavirusHumansBiology (General)PandemicsAgedHealth Services Needs and DemandQRMiddle AgedHospitalizationIntensive Care UnitsMassachusettsNonlinear DynamicsArea Under CurveFemaleCoronavirus Infections

Sustainable Development Goals

SDG 3: 3. Good healthSDG 7: 7. Clean energySDG 13: 13. Climate actionSDG 0: Good health and well-being

Additional file 1 of Accuracy of automated computer-aided risk scoring systems to estimate the risk of COVID-19: a retrospective cohort study

10.6084/m9.figshare.26708699.v1figshare

Additional file 1 of Accuracy of automated computer-aided risk scoring systems to estimate the risk of COVID-19: a retrospective cohort study

10.6084/m9.figshare.26708699figshare

Early prediction of level-of-care requirements in patients with COVID-19

FAIR Checklist

Authors (9)

Funding (8 grants)

Impact

Open Access

Topics

Linked Datasets (2)

Annotations (Europe PMC)