From Forecasting to Features: Zero-Shot Forecasting for Feature Extraction

Generative EHR models can estimate clinical risk in a zero-shot fashion by sampling future trajectories and computing outcome probabilities from generated rollouts. Yet in practice, direct use of these zero-shot risk estimates often underperforms supervised tabular baselines built from summaries of the observed past. In this work, we ask a narrower question: can future trajectories sampled from a generative foundation model be used to construct features that enhance the performance of downstream predictors? To test this, we convert model rollouts into horizon-specific \emph{generated future features} (GFFs) that summarize, for each patient, the probability of all future clinical events under the model, and train a simple XGBoost classifier that uses these features for downstream classification tasks. Across post-discharge prediction tasks in MIMIC-IV and a large private heart failure cohort, GFFs consistently achieve higher AUROC than direct zero-shot prediction and supervised learning on features derived from patient history. In our experiments we observe that stronger zero-shot forecasting performance tends to yield more performant GFFs. These results suggest that the value of current zero-shot generative EHR models is not only in their ability to generate future clinical trajectories, but also in their role as probabilistic feature generators that can be used for simple supervised risk models.