{
  "id": 447,
  "url": "https://arxiv.org/abs/2606.30702v1",
  "title": "Accelerometry-Derived Digital Biomarkers for Cardiometabolic Risk: A Population-Representative Tabular Benchmark with Uncertainty Quantification",
  "summary": "Structured tabular data dominates clinical medicine, yet existing benchmarks fail to reflect real-world properties like complex survey sampling, demographic oversampling, and subgroup fairness. We introduce the NHANES Accelerometry Cardiometabolic Benchmark, derived from NHANES 2003-2006, comprising 1,381 adults with hip-worn accelerometry, fasting laboratory biomarkers, dietary intake, and anthropometrics. We evaluate three tabular learning methods -- ridge regression, XGBoost, and the foundati",
  "authors": "Federico Felizzi",
  "category": "research",
  "topics": "bias-fairness,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-29T12:54:14.000Z",
  "fetched_at": "2026-07-14T14:14:32.647Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/447",
  "original_url": "https://arxiv.org/abs/2606.30702v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}