Autoregressive EHR Foundation Models with Multimodal Inputs
Autoregressive foundation models trained on tokenized electronic health records (EHRs) can support zero-shot clinical prediction, yet most operate on structured event codes alone, and do not incorporate multiple modalities in a principled way. We present a framework for conditioning such models on auxiliary clinical modalities, including ECG waveforms, chest X-ray images, and clinical notes, using modality-specific latent compression and gated cross-attention with temporal alignment. We investig
Record details
Published: 24 July 2026
Source: arXiv cs.LG
Category: Research
Topics: Safety & alignment · Healthcare · Finance, VC & PE
Retrieved: 27 July 2026
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ethics.ai (24 July 2026), “Autoregressive EHR Foundation Models with Multimodal Inputs,” evidence record 13736, https://ethics.ai/record/13736 (originally published by arXiv cs.LG).
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