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Predicting hypertension onset from longitudinal electronic health records with deep learning

JAMIA Open(2022)10.1093/jamiaopen/ooac097Source: DataRank Database

Predicting hypertension onset from longitudinal electronic health records with deep learning is a research paper published in JAMIA Open (2022). On theSindex it has a DataRank of 0. It has been cited 26 times.

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0DataRank · unranked

Scored on demand from live citation data

Open Access26 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (3 grants)Impact (FWCI 0.00)OA: goldTopics (11)IDs (PubMed, PMC, 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)

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Authors (7)

Ariane Morassi Sasso ORCID,Nina Kiwit,Subhronil Bose,Girish N. Nadkarni ORCID,Riccardo Miotto ORCID

Live enrichment

PubMed:36448021 PMC:PMC9696747 OpenAlex:https://openalex.org/W36448021

National Institutes of Health

Grant: S10OD026880

NIH HHS

Grant: S10 OD030463

National Institutes of Health

Grant: 1S10OD026880-01

Big Omics Data Engine 2 Supercomputer

FWCI

0.00

Citation Percentile

0.0%

goldLicense: cc-by

publisherhttps://academic.oup.com/jamiaopen/article-pdf/5/4/ooac097/47266745/ooac097.pdf

journalhttps://www.em-consulte.com/en/article/114857

repositoryhttps://publishup.uni-potsdam.de/frontdoor/index/index/docId/65410

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/9696747

Europe_PMChttps://europepmc.org/articles/PMC9696747

Fields of Study

Preterm Birth and Chorioamnionitis03 medical and health sciences0302 clinical medicine

Keywords

Political scienceHypertensionMachine LearningElectronic Health RecordsDeep Learningddc:000Hasso-Plattner-Institut für Digital Engineering gGmbHResearch and Applications

Sustainable Development Goals

SDG 3: 3. Good health