From Static Risk to Dynamic Trajectories: Toward World-Model-Inspired Clinical Prediction
The article discusses a new approach to clinical prediction that moves from static risk assessments to dynamic modeling of disease trajectories. It emphasizes the importance of intervention-aware models that account for treatment effects and patient-specific disease evolution. The authors propose a unified framework that integrates various modeling techniques to improve clinical decision-making.
- ▪Static prediction models often fail in clinical settings due to conflating disease biology with clinician behavior.
- ▪The review focuses on intervention-aware disease trajectory modeling in clinical AI, which estimates patient-specific longitudinal disease evolution.
- ▪The authors present a unified framework that bridges forecasting, counterfactual trajectories, and policy evaluation.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.16927 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | Kw-SR2oS9muB |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.16927 (cs) [Submitted on 16 May 2026] Title:From Static Risk to Dynamic Trajectories: Toward World-Model-Inspired Clinical Prediction Authors:Pujun Feng, Xiaoyu Guo, Seyed Ehsan Saffari, Min Hun Lee, Siew-Kei Lam, Erik Cambria, Xibin Sun, Yangtao Zhou, Tong Yang, Xiaoyu Zhang, Tao Tan, Yue Sun, Bin Cui View a PDF of the paper titled From Static Risk to Dynamic Trajectories: Toward World-Model-Inspired Clinical Prediction, by Pujun Feng and 12 other authors View PDF Abstract:Clinical decision-making is a feedback system where risk estimates influence treatment, which in turn changes disease trajectories, and both shape clinicians' measurement practices.
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