{
  "id": 9055384,
  "title": "A Patient World Model for Early Forecasting of Digital Health Campaign Outcomes: Capabilities and Limits",
  "url": "https://urgent.news/2026/09/20/a-patient-world-model-for-early-forecasting-of-digital-health",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-20T03:37:31.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.23333v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Digital direct-to-consumer (DTC) health campaigns are usually measured after the fact. In-flight forecasting commonly relies on a separate classifier for every cutoff and horizon. We treat this task as a dynamic-system problem and build a compact patient world model. The architecture maintains a latent state per patient, learns exposure-conditioned state dynamics jointly with a weekly conversion…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}