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

HOLMES: Health OnLine Model Ensemble Serving for Deep Learning Models in Intensive Care Units

arXiv (Cornell University)(2020)10.48550/arxiv.2008.04063Source: DataRank Database

HOLMES: Health OnLine Model Ensemble Serving for Deep Learning Models in Intensive Care Units is a research paper published in arXiv (Cornell University) (2020). On theSindex it has a DataRank of 0. It has been cited 4 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access4 citations

Free full text

Cite:

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 (5 grants)OA: greenTopics (27)IDs (PubMed, OpenAlex)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 (8)

Yanbo Xu ORCID,Alind Khare,Satria Priambada,Kevin Maher ORCID,Alaa Aljiffry ORCID

Live enrichment

PubMed:19022398 OpenAlex:https://openalex.org/W3048046405

National Science Foundation

Grant: 1533768

XPS: FULL: DSD: A Parallel Tensor Infrastructure (ParTI!) for Data Analysis

National Institutes of Health

Grant: 1R56HL138415-01

Interpretable Deep Learning Model for Longitudinal Electronic Health Records and Applications to Heart Failure Prediction

National Science Foundation

Grant: 1838042

BigData:IA:Collaborative Research: TIMES: A tensor factorization platform for spatio-temporal data

National Science Foundation

Grant: 1418511

SCH: INT: Collaborative Research: High-throughput Phenotyping on Electronic Health Records using Multi-Tensor Factorization

National Institutes of Health

Grant: 1R01NS107291-01

Big Data and Deep Learning for the Interictal-Ictal-Injury Continuum

greenLicense: https://www.acm.org/publications/policies/copyright_policy#Background

repositoryhttps://arxiv.org/pdf/2008.04063

repositoryhttp://arxiv.org/abs/2008.04063

repositoryhttps://doi.org/10.48550/arxiv.2008.04063

https://doi.org/10.1145/3394486.3403212

http://arxiv.org/pdf/2008.04063

Fields of Study

Machine Learning in HealthcareHealthcare Technology and Patient MonitoringSepsis Diagnosis and TreatmentMachine Learning (cs.LG)Machine Learning (stat.ML)FOS: Computer and information sciences0202 electrical engineering, electronic engineering, information engineering02 engineering and technology

Keywords

Computer scienceEnsemble forecastingInferenceLatency (audio)Machine learningArtificial intelligenceDeep learningTask (project management)Pipeline (software)Health carePredictive modellingBig dataData miningEngineeringFOS: Computer and information sciencesComputer Science - Machine LearningStatistics - Machine LearningMachine Learning (stat.ML)Machine Learning (cs.LG)

Sustainable Development Goals

SDG 3: 3. Good health