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

Fast and interpretable mortality risk scores for critical care patients

Journal of the American Medical Informatics Association(2024)10.1093/jamia/ocae318Source: DataRank Database

Fast and interpretable mortality risk scores for critical care patients is a research paper published in Journal of the American Medical Informatics Association (2024). On theSindex it has a DataRank of 0. It has been cited 3 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access3 citations

Download PDF

Cite:

Linked data & code

Fast and Interpretable Mortality Risk Scores for Critical Care Patients · arXiv

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 0.85)OA: greenTopics (51)Linked Datasets (1)DataCite Reuse (1)IDs (PubMed, PMC, OpenAlex)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (2/2)

Has DOI
Indexed in repositories

Accessible (1/2)

Open Access

Interoperable (2/2)

DataCite relations
Linked datasets

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 (7)

Muhang Tian,Lesia Semenova ORCID,Jiachang Liu ORCID,Jack Junchi Xu,Joseph R. Scarpa ORCID

Live enrichment

PubMed:39873685 PMC:PMC12005632 OpenAlex:https://openalex.org/W4406879452

NSF

Grant: IIS-2130250

NIH

Grant: 1R01NS131347-01A1

Comparative effectiveness of EEG-guided anti-seizure treatment in acute brain injury

NIH

Grant: 1R01HL166233-01

Novel Algorithm and Data Strategies to detect and Predict atrial fibrillation for post-stroke patients (NADSP)

NIDA

Grant: R01 DA054994

NHLBI NIH HHS

Grant: R01 HL166233

NINDS NIH HHS

Grant: R01 NS131347

FWCI

0.85

Citation Percentile

0.8%

Citation Trend

2025

2026

greenLicense: OUP Standard Publication Reuse

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC12005632/

publisherhttps://academic.oup.com/jamia/article-pdf/32/4/736/61662745/ocae318.pdf

journalhttps://doi.org/10.1093/jamia/ocae318

repositoryhttps://pubmed.ncbi.nlm.nih.gov/39873685

Fields of Study

Sepsis Diagnosis and TreatmentMachine Learning in HealthcareExplainable Artificial Intelligence (XAI)02 engineering and technology0202 electrical engineering, electronic engineering, information engineeringHumansRisk AssessmentIntensive Care UnitsMachine LearningHospital MortalityAlgorithmsCritical CareCritical Illness

MeSH Terms

Machine LearningAlgorithmsCritical CareHumansIntensive Care UnitsCritical IllnessHospital MortalityRisk Assessment

Keywords

Computer scienceIntensive care unitRegularization (linguistics)Machine learningAcute coronary syndromePredictive modellingMedicineIntensive care medicineArtificial intelligenceEmergency medicineMyocardial infarctionInternal medicineSparsityMortality RiskRisk ScoresInterpretable Machine LearningInterpretable AiFOS: Computer and information sciencesComputer Science - Machine LearningCritical CareCritical IllnessResearch and ApplicationsRisk AssessmentMachine Learning (cs.LG)Computer Science - Computers and SocietyIntensive Care UnitsComputers and Society (cs.CY)HumansHospital MortalityAlgorithms

Sustainable Development Goals

SDG 0: Good health and well-being

Fast and Interpretable Mortality Risk Scores for Critical Care Patients

10.48550/arxiv.2311.13015arXiv