arXiv Machine Learning September 22, 2026 By Yunlong Wang

A Patient World Model for Early Forecasting of Digital Health Campaign Outcomes: Capabilities and Limits

Read the original on arXiv Machine Learning →

The paper introduces a compact patient world model that forecasts digital health campaign outcomes by maintaining a latent state per patient and learning exposure‑conditioned dynamics. Evaluated on a large US campaign dataset, the model predicts new‑to‑brand prescription volume with low relative error (2.9% at week‑4 cutoff) compared to much higher errors from baseline classifiers. The study also shows that dense next‑exposure supervision is crucial for accurate forecasts when conversions are rare and highlights limitations in interpreting exposure‑conditioned rollouts causally.

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