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arXiv Machine Learning By Ashvin Gupta, Joshua Placidi, Brendan Delaney, Alessandra Russo

Block Diffusion for Longitudinal EHR: Accuracy, Efficiency, and Representation Trade-offs

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The paper evaluates block-based masked diffusion (BD) as an alternative to autoregressive (AR) models for zero‑shot prediction from longitudinal electronic health records. On the MIMIC‑IV dataset, BD shows a clear trade‑off between quality and efficiency: small‑block configurations retain most of AR’s predictive performance while enabling faster inference, with a representative B=8 setting achieving about 1.43× faster evaluation. Although BD’s generative rollout lags behind AR in most settings, frozen linear probes on BD and AR representations perform nearly identically across clinical prediction tasks, indicating that BD learns comparably informative clinical representations.

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