{
  "id": 6057,
  "url": "https://arxiv.org/abs/2605.04063v1",
  "title": "Investigating Trustworthiness of Nonparametric Deep Survival Models for Alzheimer's Disease Progression Analysis",
  "summary": "Alzheimer's Dementia (AD) is a progressive neurodegenerative disease marked by irreversible decline, making reliable modeling of its progression essential for effective patient care. Progression-aware methods such as survival analysis are therefore crucial tools for the early detection and monitoring of AD. Recent advancements in deep learning have demonstrated remarkable performance in survival tasks, but alarmingly fewer studies have been conducted in the domain of AD. Further, the studies tha",
  "authors": "Jacob Thrasher, Kaitlyn Heintzelman, Peter Martone, David Kotlowski, Binod Bhattarai, Donald Adjeroh et al.",
  "category": "research",
  "topics": "healthcare,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-10T16:41:48.000Z",
  "fetched_at": "2026-07-14T16:32:11.185Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6057",
  "original_url": "https://arxiv.org/abs/2605.04063v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}