患者世界模型：第4周即可预测全年健康广告转化量
A Patient World Model for Early Forecasting of Digital Health Campaign Outcomes: Capabilities and Limits
广告效果不用等投放结束，这篇论文第4周就能把全年处方量预测误差压到2.9%，梯度提升基线是13%起步。
这篇 arXiv 论文把数字直销（DTC）健康广告的效果预测当作动态系统问题，为每位患者维护一个潜状态，联合学习曝光条件下的状态转移与每周转化风险。
在包含 147,173 名患者、520 万风险人周的美国广告数据集上，模型从第 4 周截点预测到第 52 周的新品牌处方量，相对误差仅 2.9%，第 8–26 周截点为 0.
8–2.6%。
同等信息下的 pooled-hazard 梯度提升基线相对误差为 13.6–33.
1%，逐时点分类器表现更差。消融实验显示，去掉受 Fisher 信息分析启发的下一次曝光密集监督目标后，处方量误差扩大约 2–14 倍。
情景模拟中关闭全部未来曝光，预测转化率反而从 0.31 升到 0.
89，作者据此指出曝光条件化的预测不能当作因果解释。
A Patient World Model for Early Forecasting of Digital Health Campaign Outcomes: Capabilities and Limits
Digital direct-to-consumer (DTC) health campaigns are usually measured after the fact. In-flight forecasting commonly relies on a separate classifier for every cutoff and horizon. We treat this task as a dynamic-system problem and build a compact patient world model. The architecture maintains a latent state per patient, learns exposure-conditioned state dynamics jointly with a weekly conversion hazard, and rolls forward into future conversion curves. We evaluate it on a US campaign dataset with 147{,}173 patients and 5.2 million at-risk person-weeks. In a retrospective evaluation conditioned on recorded future exposures, the model forecasts the remaining new-to-brand prescription volume through week 52 with a relative error of 2.9\% from a week-4 cutoff and 0.8--2.6\% from cutoffs at weeks 8--26. The strongest non-recurrent baseline, a pooled-hazard gradient boosting model given the same survival rollout and information, has relative errors of 13.6--33.1\%. Per-horizon classifiers perform substantially worse. A Fisher-information analysis motivates dense next-exposure supervision when conversions are rare. Removing this auxiliary objective increases prescription-volume error by approximately $2$--$14\times$, while providing no consistent disadvantage on the more common specialist-visit outcome. We also evaluate scenario simulation. Switching all future exposure off raises predicted conversion from 0.31 to 0.89, a pattern consistent with selection effects in observational exposure data. This result highlights the limits of interpreting exposure-conditioned rollouts causally.
This report is categorized under AITOP's "Papers" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Key entities and technical topics under continuous tracking include 世界模型, 数字健康, 处方预测, 广告投放.
Source attribution: curated from arXiv cs.AI (@Yunlong Wang).
