Scaling Recurrence-aware Foundation Models for Clinical Records via Next-Visit Prediction
While large-scale pretraining has revolutionized language modeling, its potential remains underexplored in healthcare with structured electronic health records (EHRs). We present RAVEN, a novel generative pretraining strategy for sequential EHR data based on Recurrence-Aware next-Visit EveNt prediction. Leveraging a dataset of over one million unique individuals, our model learns to autoregressively generate tokenized clinical events for the next visit conditioned on patient history. We introduce regularization on predicting repeated events and highlight a key pitfall in EHR-based foundation model evaluations: repeated event tokens can inflate performance metrics when new onsets are not d...
| Authors | Haresh Rengaraj Rajamohan, Xiang Gao, Weicheng Zhu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 25 March 2026 |
| Last updated at source | 20 April 2026 |
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