{
  "id": 4014,
  "url": "https://arxiv.org/abs/2605.22859v1",
  "title": "Staging by the Book: Automatic Sleep Stage Classification Using Scoring Rules",
  "summary": "Automated sleep staging is commonly approached as a supervised machine learning problem, with deep learning methods dominating recent research. While machine learning models achieve near-human level agreement with human-scored reference sleep stages, their decisions are typically opaque and not designed to follow clinical scoring rules. We propose a transparent alternative: a deterministic, rule-based sleep staging method that explicitly operationalizes the American Academy of Sleep Medicine's (",
  "authors": "Emil Hardarson, Konstantin Popov, Sigridur Sigurdardottir, Anna Sigridur Islind, Erna Sif Arnardóttir, María Óskarsdóttir",
  "category": "research",
  "topics": "healthcare,transparency",
  "orgs": null,
  "regions": "us",
  "published_at": "2026-05-19T15:13:35.000Z",
  "fetched_at": "2026-07-14T16:30:41.582Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4014",
  "original_url": "https://arxiv.org/abs/2605.22859v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}