Pilot corpus: NIH-funded biomedical datasets. Any DOI can be scored on demand — network effects sharpen as coverage grows.

Prediction of extubation failure among low birthweight neonates using machine learning

Journal of Perinatology(2023)10.1038/s41372-022-01591-3Source: DataRank Database

Prediction of extubation failure among low birthweight neonates using machine learning is a research paper published in Journal of Perinatology (2023). On theSindex it has a DataRank of 0. It has been cited 19 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access19 citations

Download PDF

Cite:

Linked data & code

doi

DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?

›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (3 grants)Impact (FWCI 3.25)OA: greenTopics (46)IDs (PubMed, PMC, OpenAlex)Annotations (1)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)

FAIR checklist signals are shown for context only and do not affect DataRank scoring.

Run a calibrated FAIR evaluation for this paper →

We only score data papers we can read in full — never from an abstract alone.

Authors (6)

Grace Lam ORCID,Jingyi Liu ORCID,Andrew L. Beam ORCID,Kristyn Beam ORCID,Jonathan C. Levin ORCID

Live enrichment

PubMed:36611107 PMC:PMC10348822 OpenAlex:https://openalex.org/W4313678736

NHLBI NIH HHS

Grant: L40 HL159791

NHLBI NIH HHS

Grant: K01 HL141771

National Institutes of Health

Grant: 7K01HL141771-02

Predicting Pulmonary and Cardiac Morbidity in Preterm Infants with Deep Learning

FWCI

3.25

Citation Percentile

0.9%

Influential Citations

Citation Trend

2023

2024

2025

2026

greenLicense: other-oa

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC10348822/pdf/nihms-1913006.pdf

GREENhttps://pmc.ncbi.nlm.nih.gov/articles/PMC10348822/pdf/nihms-1913006.pdf

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC10348822/pdf/nihms-1913006.pdf

publisherhttps://www.nature.com/articles/s41372-022-01591-3.pdf

publisherhttps://www.nature.com/articles/s41372-022-01591-3

Fields of Study

Neonatal Respiratory Health ResearchRespiratory Support and MechanismsHealthcare Technology and Patient MonitoringMedicine0301 basic medicine03 medical and health sciences0302 clinical medicineInfant, NewbornHumansVentilator WeaningRetrospective StudiesBirth WeightAirway ExtubationRespiration, Artificial

MeSH Terms

Birth WeightHumansInfant, NewbornRespiration, ArtificialRetrospective StudiesVentilator WeaningAirway Extubation

Keywords

MedicineLogistic regressionGestational ageMechanical ventilationGeneralizability theoryDemographicsEmergency medicineCohortVital signsRetrospective cohort studyPediatricsIntensive care medicineAnesthesiaPregnancySurgeryInternal medicineStatisticsDemographyInfant, NewbornAirway ExtubationHumansBirth WeightVentilator WeaningRespiration, ArtificialRetrospective Studies

Sustainable Development Goals

SDG 0: Good health and well-being

Prediction of extubation failure among low birthweight neonates using machine learning

FAIR Checklist

Authors (6)

Funding (3 grants)

Impact

Open Access

Topics

Annotations (Europe PMC)