Salvato in:
| Autori principali: | , , , , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2605.16927 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866911690959880192 |
|---|---|
| author | Feng, Pujun Guo, Xiaoyu Saffari, Seyed Ehsan Lee, Min Hun Lam, Siew-Kei Cambria, Erik Sun, Xibin Zhou, Yangtao Yang, Tong Zhang, Xiaoyu Tan, Tao Sun, Yue Cui, Bin |
| author_facet | Feng, Pujun Guo, Xiaoyu Saffari, Seyed Ehsan Lee, Min Hun Lam, Siew-Kei Cambria, Erik Sun, Xibin Zhou, Yangtao Yang, Tong Zhang, Xiaoyu Tan, Tao Sun, Yue Cui, Bin |
| contents | Clinical decision-making is a feedback system where risk estimates influence treatment, which in turn changes disease trajectories, and both shape clinicians' measurement practices. Static prediction often fails clinically: models trained on observational care logs conflate disease biology with clinician behavior, particularly under treatment confounder feedback and irregular or informative observation. This Review focuses on intervention-aware disease trajectory modeling in clinical AI--methods estimating patient-specific longitudinal disease evolution and assessing trajectory changes under alternative treatments. We organize the field around six linked components: three decision tasks (factual forecasting, counterfactual estimation, policy evaluation) and three data-generating mechanisms (disease evolution, treatment assignment, observation process) that determine identifiability. We present the first unified framework bridging forecasting, counterfactual trajectories, and policy evaluation across discrete/continuous time, explicitly addressing treatment assignment, time-varying confounding, and observation bias. We synthesize key method families (multistate/joint models, temporal point-process, deep sequence architectures, longitudinal causal inference), map them to relevant components, and align evaluation with claim strength via overlap diagnostics, uncertainty quantification, off-policy robustness, and target-trial validation. This synthesis advances benchmark prediction to decision-grade clinical evidence, enabling treatment-sensitive individualized futures, pre-deployment policy stress-testing, and safer closed-loop learning health systems that adapt/abstain when evidence is insufficient. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_16927 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | From Static Risk to Dynamic Trajectories: Toward World-Model-Inspired Clinical Prediction Feng, Pujun Guo, Xiaoyu Saffari, Seyed Ehsan Lee, Min Hun Lam, Siew-Kei Cambria, Erik Sun, Xibin Zhou, Yangtao Yang, Tong Zhang, Xiaoyu Tan, Tao Sun, Yue Cui, Bin Artificial Intelligence Clinical decision-making is a feedback system where risk estimates influence treatment, which in turn changes disease trajectories, and both shape clinicians' measurement practices. Static prediction often fails clinically: models trained on observational care logs conflate disease biology with clinician behavior, particularly under treatment confounder feedback and irregular or informative observation. This Review focuses on intervention-aware disease trajectory modeling in clinical AI--methods estimating patient-specific longitudinal disease evolution and assessing trajectory changes under alternative treatments. We organize the field around six linked components: three decision tasks (factual forecasting, counterfactual estimation, policy evaluation) and three data-generating mechanisms (disease evolution, treatment assignment, observation process) that determine identifiability. We present the first unified framework bridging forecasting, counterfactual trajectories, and policy evaluation across discrete/continuous time, explicitly addressing treatment assignment, time-varying confounding, and observation bias. We synthesize key method families (multistate/joint models, temporal point-process, deep sequence architectures, longitudinal causal inference), map them to relevant components, and align evaluation with claim strength via overlap diagnostics, uncertainty quantification, off-policy robustness, and target-trial validation. This synthesis advances benchmark prediction to decision-grade clinical evidence, enabling treatment-sensitive individualized futures, pre-deployment policy stress-testing, and safer closed-loop learning health systems that adapt/abstain when evidence is insufficient. |
| title | From Static Risk to Dynamic Trajectories: Toward World-Model-Inspired Clinical Prediction |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2605.16927 |