{
  "id": 17324,
  "url": "https://arxiv.org/abs/2608.06366v1",
  "title": "Tracing the Heart: An Evidence-Linked Pipeline for Heart-Failure Feature Engineering",
  "summary": "Electronic health record (EHR) feature engineering is a major bottleneck in clinical research and AI, accounting for 39-45% of data scientists' workload. This is especially pronounced in heart failure, which affects an estimated 6.7 million U.S. adults and requires integrating fragmented EHR data with disease-specific, guideline-based clinical reasoning. Existing rule-based and large language model (LLM)-based approaches offer only partial automation with limited maintainability and evidence tra",
  "authors": "Soorya Ram Shimgekar, Michelle Hu, Dorisa Shehi, Daniel Kang, Roy Ka-Wei Lee, Koustuv Saha, Christian Poellabauer, Christopher Lee, Sajeev Singh, Piyum Zonooz, Navin Kumar, Zeeshan Ahmed, Priyadarshini Kachroo",
  "category": "research",
  "topics": "jobs-economy,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-06T17:57:37.000Z",
  "fetched_at": "2026-08-07T05:10:58.501Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/17324",
  "original_url": "https://arxiv.org/abs/2608.06366v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}