Autoregressive EHR Foundation Models with Multimodal Inputs
Score
79系统性地提出了多模态EHR模型的设计空间和消融结论，对领域有显著增量启发。
实验设计完整，消融对比清晰，但缺乏在更多数据集上的验证。
为临床AI系统的多模态融合提供了具体可复现的设计指南。
论文刚刚发布（2026年7月），紧跟多模态基础模型热潮。
arXiv预印本，但来自公认AI领域，且使用公开数据集MIMIC-IV。
Base 87.5 × T2 Source weight ×0.9 = 79
Curation line 70Selected
Why selected
The paper presents innovative methodology and rigorous experimental design, offering significant reference value for the multimodal EHR field.
AI commentary
「This paper is highly valuable for researchers and engineers building multimodal clinical prediction models. It not only provides concrete architectural choices (latent compression outperforms no compression) but also sounds a warning: stacking modalities does not guarantee performance gains and can even hurt. I especially appreciate the authors' honesty in reporting negative results—exactly what real-world deployment needs. If you are trying to incorporate images or text into EHR models, this is a must-read.」
Quick overview
This paper presents a framework that enables autoregressive EHR foundation models to incorporate auxiliary modalities such as ECG waveforms, chest X-ray images, and clinical notes through modality-specific latent compression and gated cross-attention with temporal alignment. Controlled ablations on MIMIC-IV show that the best latent-compression configuration outperforms both uncompressed cross-attention and mean pooling, and that encoder choice has a clear within-modality effect. However, simply adding auxiliary modalities does not guarantee improvement on ICU mortality prediction over an EHR-only baseline, highlighting the need for careful fusion architecture design and clinical context evaluation.
- Proposes a multimodal framework using latent compression and gated cross-attention to extend autoregressive EHR models.
- Ablations on ECG, X-ray, and clinical notes show the best latent-compression configuration outperforms traditional methods.
- Encoder quality significantly affects within-modality performance, but multimodal fusion does not guarantee improved downstream tasks.
- Emphasizes that multimodal EHR model design requires careful evaluation in clinical contexts.
Full text
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