{
  "id": 7244243,
  "title": "DynSHAP: Towards Explainable Dynamic Survival Analysis",
  "url": "https://urgent.news/2026/09/11/dynshap-towards-explainable-dynamic-survival-analysis",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-11T16:39:55.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.13042v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Deep learning models for dynamic survival analysis (DSA) achieve strong predictive performance by incorporating longitudinal patient data, but their black box nature limits clinical trust and adoption. Existing explainability methods cannot handle longitudinal, irregular inputs and functional survival outputs simultaneously, which limits their usability in DSA. We propose DynSHAP, a SHAP…",
  "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."
}