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Validation of an internationally derived patient severity phenotype to support COVID-19 analytics from electronic health record data

Journal of the American Medical Informatics Association(2021)10.1093/jamia/ocab018Source: DataRank Database

Validation of an internationally derived patient severity phenotype to support COVID-19 analytics from electronic health record data is a dataset published in Journal of the American Medical Informatics Association (2021). On theSindex it has a DataRank of 1.6, placing it in the top 12.8% of the data-sharing corpus. It has been cited 52 times, with 23 citing works in its 1-hop citation network.

Top 13%percentile

1.6DataRank

Ranks in the top 13% for downstream scientific impact

Data paper Open Access52 citations · 23 citing works in its network

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (24 grants)Impact (FWCI 6.24)OA: hybridTopics (41)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 (1/3)

Dataset classification

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DataRank Breakdown

Base Score 37%Citation Network 63%

Base Score Contribution

0.596

From this paper's citation signal

Citation Network Contribution

1.0

From 17 citing papers with measurable signal

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Top 5 citers driving the network score

Ranked by each citer's contribution to N(p) — log1p(C_q) divided by its reference count — out of 23 citers.

Biomedical and health informatics continue to contribute to COVID-19 pandemic solutions and beyond
Journal of the American Medical Informatics Association20212 citations5 referencesContributes 0.220
COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records
The Lancet Digital Health202284 citations32 referencesContributes 0.139
Understanding enterprise data warehouses to support clinical and translational research: enterprise information technology relationships, data governance, workforce, and cloud computing
Journal of the American Medical Informatics Association202121 citations30 referencesContributes 0.103
Automated provision of clinical routine data for a complex clinical follow-up study: A data warehouse solution
Health Informatics Journal202211 citations27 referencesContributes 0.092
A robust and parsimonious machine learning method to predict ICU admission of COVID-19 patients
Medical & Biological Engineering & Computing202227 citations38 referencesContributes 0.088

Authors (41)

Hossein Estiri ORCID,Griffin M. Weber ORCID,Bertrand Moal ORCID,Paul Avillach ORCID,Chuan Hong ORCID

Live enrichment

PubMed:33566082 PMC:PMC7928835 OpenAlex:https://openalex.org/W3127851201

National Institute of Neurological Disorders and Stroke

Grant: R01NS098023

NIH Eunice Kennedy Shriver National Institute of Child Health and Human Development

Grant: K12 HD047349

National Heart, Lung, and Blood Institute

Grant: K23-HL148394

National Heart, Lung, and Blood Institute

Grant: L40-HL148910

National Heart, Lung, and Blood Institute

Grant: U01HL121518