{
  "id": 16633,
  "url": "https://arxiv.org/abs/2608.04016",
  "title": "AI-driven Multimodal Representation Learning for Latent Mediation Structure Discovery of Socioeconomic Disadvantage, Psychosocial Factors, and Cardiometabolic Multimorbidity: Insights from the All of Us Research Program",
  "summary": "arXiv:2608.04016v1 Announce Type: new Abstract: Social disadvantage is associated with multimorbidity, but the pathways linking social conditions to disease burden remain poorly understood. We developed an AI-driven multimodal mediation framework that integrates socioeconomic, psychosocial, clinical, laboratory, behavioral, and genomic data from the All of Us Research Program. Modality-specific variational autoencoders were used to derive latent representations of each data domain, and mediation",
  "authors": "Cong Cao, Shuangge Ma",
  "category": "research",
  "topics": "healthcare,biotech",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-06T04:00:00.000Z",
  "fetched_at": "2026-08-06T05:10:11.148Z",
  "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/16633",
  "original_url": "https://arxiv.org/abs/2608.04016",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}