从预测到行动：面向数字医学的人群健康世界模型（PHWM）
From Prediction to Action: Population Health World Model for Digital Medicine
AI 导读
arXiv 论文提出人群健康世界模型（PHWM），主张数字医学的下一步不是更精准的预测，而是表示行动如何改变健康结果。文章指出当前建模局限于任务、结局和预测导向，PHWM 需补齐动态健康状态建模、人群级干预智能、验证与公平治理三大结构缺口，并依赖行动依赖观测下的表示学习、多尺度干预智能与显式决策边界验证三项能力。
正文 · 原文
Abstract:Digital medicine now captures data that traces individual health trajectories and reveals population health dynamics through electronic health records, claims, remote monitoring, wearable devices, mobile devices, social sensing data, medical images, etc. But much of its modeling still centers on task-specific, outcome-specific, and prediction-specific estimation. The next frontier is not simply better prediction, but action in an evolving health world shaped by multiple decision-makers, heterogeneous populations, resource constraints, and feedback in care delivery. This is why digital medicine now needs world models: not only to estimate what will happen, but to represent how actions change what can happen next -- whom to prioritize, how to allocate scarce resources, how to adapt care across subgroups, and how interventions reshape future health trajectories and population dynamics.
We call such a model population health world model (PHWM): a more foundational representation of an evolving health world in which health states, individual trajectories, population health dynamics, interventions, constraints, and feedback are intrinsically coupled. Building a PHWM endows three unmet structural gaps in current digital medicine: dynamic health-state modeling, population-level intervention intelligence, and validation, equity, and governance for action-oriented use. We argue that addressing these gaps requires three minimal capabilities: representation learning under action-dependent observation, multi-scale intervention intelligence, and validation with explicit decision boundaries, while highlighting the importance of social, contextual, and public-health sensing data for action-oriented digital medicine.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.24662 [cs.AI] |
| (or arXiv:2610.24662v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.24662 arXiv-issued DOI via DataCite |
Submission history
From: Xike Xie [view email]
[v1]
Sat, 17 Oct 2026 12:34:53 UTC (1,332 KB)
[v2]
Fri, 30 Oct 2026 01:34:54 UTC (1,332 KB)
来源：arXiv cs.AI · arxiv.org