datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Resume-Screening-DatasetResume-Screening-Datasetclinical-quad-enrollment-criteria-drift-site-selection-bias-screening-pressure-v0.1Clarus Clinical Quad Coupling Enrollment Criteria Drift Site Selection Bias Screening Pressure v0.1
PurposeDetect enrollment population drift driven by four interacting nodes.
Quad nodes
Criteria relaxation or documentation gap
Site selection or recruitment bias
Screening workflow pressure
Governance or interim timing pressure
InputOne vignette.
OutputStrict JSON only.
Required keys
enrollment_drift_risk
risk_type
driver_nodes
recommended_action
action_detail
rationale… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-enrollment-criteria-drift-site-selection-bias-screening-pressure-v0.1.Resume-Screening-Datasetafrica-synth-cancer-cancer-screening-programs-africa-all
Cancer Screening Programs Africa | Africa (Electric Sheep Africa metadata inventory)
Size category: 10K<n<100K - Formats: csv - Sector: health - Engineered by Electric Sheep Africa
TL;DR
This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.
What This Dataset Covers
Health datasets help… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-cancer-cancer-screening-programs-africa-all.africa-synth-cancer-screening-cervical-kenya-kenya
Cervical Cancer Screening - Kenya | Africa (Electric Sheep Africa metadata inventory)
Size category: 10K<n<100K - Formats: csv - Sector: health - Engineered by Electric Sheep Africa
TL;DR
This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.
What This Dataset Covers
Health datasets help… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-cancer-screening-cervical-kenya-kenya.synthetic-diabetes-hypertension-NCD-screening-WHO-HEARTS
Synthetic Diabetes & Hypertension NCD Screening Dataset (Adults 18–80)
Abstract
This dataset provides 30,000 synthetic records (10,000 per scenario) of adults undergoing NCD screening at LMIC health facilities or community campaigns. Each record contains 27 variables including demographics, anthropometry (BMI, waist circumference), lifestyle risk factors (smoking, alcohol, physical activity), family history, clinical measurements (blood pressure, fasting glucose, HbA1c… See the full description on the dataset page: https://huggingface.co/datasets/koushik1212/synthetic-diabetes-hypertension-NCD-screening-WHO-HEARTS.clinical-sepsis-screening-antibiotic-escalation-coherence-risk-v0.1What this repo is for
Detect when sepsis signals
and screening plus antibiotic escalation
fall out of alignment
before
delayed treatment
and avoidable deterioration.
Resume-Screening-Datasethealth-screenings-provided-to-medicaid-and-chip-be
Health Screenings Provided to Medicaid and CHIP Beneficiaries Under Age 19
Description
This data set includes monthly counts and rates (per 1,000 beneficiaries) of health screenings provided to Medicaid and CHIP beneficiaries under the age of 19 (as of the first day of the month) by state.
These metrics are based on data in the T-MSIS Analytic Files (TAF). Some states have serious data quality issues for one or more months, making the data unusable for calculating… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/health-screenings-provided-to-medicaid-and-chip-be.ai-resume-screening-datasetclinical-quad-ai-screening-eligibility-automation-demographic-skew-outcome-bias-v0.1Clinical Quad AI Screening Eligibility Automation Demographic Skew Outcome Bias v0.1
Each row is a site monthly snapshot.
Core quad
AI screening adoptionEligibility automation strictnessDemographic skewOutcome bias
Target
label_regulatory_concern_next_90d
Files
data/train.csvdata/tester.csvscorer.py
Evaluation
Run model on data/tester.csvReturn predictions row alignedScore with scorer.py
License
MIT
Resume-Screening-DatasetResume-Screening-Dataset