arXiv cs.AI· Xike Xie, Yu Li, Weizhong Yang, Amalie Dyda, Shaohua Kevin Zhou·· 11 小时前AI 评分22
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
研究者提出人群健康世界模型（PHWM），将健康状态、个体轨迹、人群动态、干预措施、资源约束与反馈耦合建模，推动数字医学从任务特定的预测转向行动导向。该框架针对动态健康状态建模、人群级干预智能、以及面向行动的验证、公平与治理三个结构性缺口，提出动作依赖观测下的表征学习、多尺度干预智能和带明确决策边界的验证三项最小能力。
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