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A Responsible Framework for Assessing, Selecting, and Explaining Machine Learning Models in Cardiovascular Disease Outcomes Among People With Type 2 Diabetes: Methodology and Validation Study

JMIR Medical Informatics(2025)10.2196/66200Source: DataRank Database

A Responsible Framework for Assessing, Selecting, and Explaining Machine Learning Models in Cardiovascular Disease Outcomes Among People With Type 2 Diabetes: Methodology and Validation Study is a research paper published in JMIR Medical Informatics (2025). On theSindex it has a DataRank of 0. It has been cited 8 times.

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Open Access8 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (4 grants)Impact (FWCI 11.17)OA: goldTopics (49)IDs (PubMed, PMC, OpenAlex)Annotations (1)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)

Che-Yi Liao ORCID,Esmaeil Keyvanshokooh ORCID,Hui Shao ORCID,Mary Beth Weber ORCID,Francisco J. Pasquel ORCID

Live enrichment

PubMed:40577645 PMC:PMC12256707 OpenAlex:https://openalex.org/W4411724417

NIH HHS

Grant: OT2 OD032581

NIDDK NIH HHS

Grant: P30 DK111024

National Institutes of Health

Grant: 3P30DK111024-07S2

Georgia Center for Diabetes Translation Research

National Institutes of Health

Grant: 3OT2OD032581-01S6

AIM-AHEAD Coordinating Center - All Four Cores

FWCI

11.17

Citation Percentile

1.0%

Citation Trend

2025

2026

goldLicense: cc-by

journalhttps://doi.org/10.2196/66200

publisherhttps://doi.org/10.2196/66200

repositoryhttps://pubmed.ncbi.nlm.nih.gov/40577645

repositoryhttps://doaj.org/article/3fe79f39f9b841708adcefe53bb78c85

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/12256707

Fields of Study

Explainable Artificial Intelligence (XAI)Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare02 engineering and technology0202 electrical engineering, electronic engineering, information engineering

MeSH Terms

Machine LearningAgedCardiovascular DiseasesDiabetes Mellitus, Type 2FemaleHumansMaleMiddle AgedRisk Assessment

Keywords

InterpretabilityMachine learningArtificial intelligenceComputer scienceRandom forestPopulationPredictive modellingDecision treeMedicinePredictionMyocardial infarctionDiabetesCardiovascular diseaseType 2 diabetesStrokeClinical practiceCardiologyFairnessMicardiovascularT2dInterpretable Machine LearningExplainabilityResponsible FrameworkMaleOriginal PaperComputer applications to medicine. Medical informaticsR858-859.7Middle AgedRisk AssessmentDiabetes Mellitus, Type 2Cardiovascular DiseasesHumansFemaleAged

Sustainable Development Goals

SDG 0: Partnerships for the goals

A Responsible Framework for Assessing, Selecting, and Explaining Machine Learning Models in Cardiovascular Disease Outcomes Among People With Type 2 Diabetes: Methodology and Validation Study

FAIR Checklist

Authors (7)

Funding (4 grants)

Impact

Open Access

Topics

Annotations (Europe PMC)