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

arXiv:2609.23333 · cs.LG, cs.AI · Submitted 2026-09-20 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-09-20

Updated: 2026-09-20

Comments: 17 pages including supplementary

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• World Model — 54× in this paper · explained in 7 episodes
• AUROC — 10× in this paper · explained in 2 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Calibration — 7× in this paper · explained in 7 episodes
• Foundation Model — 4× in this paper · explained in 3 episodes
• Foundation Models — 3× in this paper · explained in 7 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Transformer — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Fisher Information — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Systematic Bias — 1× in this paper · explained in 3 episodes

Sources

• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
• World Models
• GAIA-1: A Generative World Model for Autonomous Driving

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