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

SynthA1c: Towards Clinically Interpretable Patient Representations for Diabetes Risk Stratification

Lecture notes in computer science(2023)10.1007/978-3-031-46005-0_5Source: DataRank Database

SynthA1c: Towards Clinically Interpretable Patient Representations for Diabetes Risk Stratification is a research paper published in Lecture notes in computer science (2023). On theSindex it has a DataRank of 0. It has been cited 2 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access2 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 (6 grants)Impact (FWCI 0.44)OA: greenTopics (34)IDs (PubMed, PMC, OpenAlex)Annotations (1)SDGs (2)

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 (11)

Allison Chae ORCID,Matthew T. MacLean ORCID,Anurag Verma ORCID,Jeffrey Duda ORCID,James C. Gee ORCID

Live enrichment

PubMed:38957550 PMC:PMC11217801 OpenAlex:https://openalex.org/W4387430192

NHLBI NIH HHS

Grant: R01 HL137984

NIBIB NIH HHS

Grant: T32 EB009384

National Institutes of Health

Grant: 1R01HL137984-01A1

Cardiac MR imaging of hemorrhagic reperfusion injury after myocardial infarction

National Institutes of Health

Grant: 2T32GM007170-29

Medical Scientist Training Program

National Institutes of Health

Grant: 5T32EB009384-09

Training Program in Biomedical Imaging and Informational Sciences

National Institutes of Health

Grant: 3UL1TR001878-06S1

Institutional Clinical and Translational Science Award

FWCI

0.44

Citation Percentile

0.6%

Influential Citations

Citation Trend

2024

2025

greenLicense: Springer Nature TDM

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC11217801/pdf/nihms-2004375.pdf

GREENhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11217801

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC11217801/pdf/nihms-2004375.pdf

publisherhttps://link.springer.com/content/pdf/10.1007/978-3-031-46005-0_5

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/11217801

Fields of Study

Machine Learning in HealthcareBiomedical Text Mining and OntologiesDiabetes Management and EducationComputer ScienceEngineeringBiologyMedicine02 engineering and technology03 medical and health sciences0302 clinical medicine0202 electrical engineering, electronic engineering, information engineering

Keywords

Risk stratificationComputer scienceStratification (seeds)Artificial intelligenceDiabetes mellitusMachine learningMedicineInternal medicineEndocrinologyDisease PredictionRepresentation LearningRadiomicsFOS: Computer and information sciencesComputer Science - Machine LearningComputer Science - Artificial IntelligenceImage and Video Processing (eess.IV)Electrical Engineering and Systems Science - Image and Video ProcessingQuantitative Biology - Quantitative MethodsMachine Learning (cs.LG)Artificial Intelligence (cs.AI)FOS: Biological sciencesFOS: Electrical engineering, electronic engineering, information engineeringQuantitative Methods (q-bio.QM)

Sustainable Development Goals

SDG 3: 3. Good healthSDG 0: Good health and well-being

SynthA1c: Towards Clinically Interpretable Patient Representations for Diabetes Risk Stratification

FAIR Checklist

Authors (11)

Funding (6 grants)

Impact

Open Access

Topics

Annotations (Europe PMC)