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Assessing Glaucoma Progression Using Machine Learning Trained on Longitudinal Visual Field and Clinical Data

Ophthalmology(2020)10.1016/j.ophtha.2020.12.020Source: DataRank Database

Assessing Glaucoma Progression Using Machine Learning Trained on Longitudinal Visual Field and Clinical Data is a research paper published in Ophthalmology (2020). On theSindex it has a DataRank of 0. It has been cited 100 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access100 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: greenTopics (39)IDs (PubMed)

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 (3)

Jithin Yohannan ORCID,Michael V. Boland ORCID,Avyuk Dixit

Live enrichment

PubMed:33359887

National Eye Institute

Grant: EY001765

NEI NIH HHS

Grant: P30 EY001765

greenLicense: http://www.elsevier.com/open-access/userlicense/1.0/

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/8222148

publisherhttps://api.elsevier.com/content/article/PII:S0161642020311969?httpAccept=text/xml

publisherhttps://api.elsevier.com/content/article/PII:S0161642020311969?httpAccept=text/plain

Fields of Study

AgedAlgorithmsClinical CompetenceDisease ProgressionFemaleFollow-Up StudiesGlaucomaGlaucoma, Open-AngleHumansIntraocular PressureMachine LearningMaleOphthalmologistsROC CurveRetrospective StudiesVisual Fields

MeSH Terms