{
  "id": 4995,
  "url": "https://arxiv.org/abs/2605.03212v2",
  "title": "ADAPTS: Agentic Decomposition for Automated Protocol-agnostic Tracking of Symptoms",
  "summary": "Modeling latent clinical constructs from unconstrained clinical interactions is a unique challenge in affective computing. We present ADAPTS (Agentic Decomposition for Automated Protocol-agnostic Tracking of Symptoms), a framework for automated rating of depression and anxiety severity using a mixture-of-agents LLM architecture. This approach decomposes long-form clinical interviews into symptom-specific reasoning tasks, producing auditable justifications while preserving temporal and speaker al",
  "authors": "Alexandria K. Vail, Marcelo Cicconet, Katie Aafjes-van Doorn, Ryan Maroney, Marc Aafjes",
  "category": "research",
  "topics": "privacy-surveillance,healthcare,agents-autonomy,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-04T23:08:42.000Z",
  "fetched_at": "2026-07-14T16:31:26.335Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4995",
  "original_url": "https://arxiv.org/abs/2605.03212v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}