{
  "id": 16210,
  "url": "https://arxiv.org/abs/2408.02677",
  "title": "Patient-centered data science: an integrative framework for evaluating and predicting clinical outcomes in the digital health era",
  "summary": "arXiv:2408.02677v2 Announce Type: replace-cross Abstract: This study proposes a novel, integrative framework for patient-centered data science in the digital health era. We developed a multidimensional model that combines traditional clinical data with patient-reported outcomes, social determinants of health, and multi-omic data to create comprehensive digital patient representations. Our framework employs a multi-agent artificial intelligence approach, utilizing various machine learning techniq",
  "authors": "Mohsen Amoei, Dan Poenaru",
  "category": "research",
  "topics": "healthcare,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-05T04:00:00.000Z",
  "fetched_at": "2026-08-05T05:10:44.550Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/16210",
  "original_url": "https://arxiv.org/abs/2408.02677",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}