{
  "id": 453,
  "url": "https://arxiv.org/abs/2606.29907v1",
  "title": "CW-B: Class Weighted Boosting Framework for Imbalance Resilient Multi Class Cardiac Phenotyping",
  "summary": "Cardiac discharge phenotyping informs post-discharge treatment and follow-up, but real-world records are often incomplete and class-imbalanced, increasing the risk of missed high-risk phenotypes. We propose CW-B, a clinical risk-aligned class-weighted XGBoost pipeline for five-class cardiac discharge phenotyping under real-world class imbalance and missingness. CW-B combines fold-specific class-balanced instance weighting, missingness-indicator augmentation, and classwise error auditing to impro",
  "authors": "Sijia Li, Xiaoyu Tan, Chen Zhan, Yuanji Ma, Haoyu Wang, Xihe Qiu",
  "category": "research",
  "topics": "healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-29T07:43:15.000Z",
  "fetched_at": "2026-07-14T14:14:32.648Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/453",
  "original_url": "https://arxiv.org/abs/2606.29907v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}