Guideline-Anchored Multimodal Fusion with Prototype Mining for Reliable EHR Analysis

Multimodal Electronic Health Records (EHRs), which integrate multivariate physiological time-series data with free-text clinical notes, provide rich temporal features for healthcare analytics. Capturing the corresponding temporal dynamics across different modalities is crucial for enhancing the accuracy of clinical outcome prediction. However, existing approaches predominantly capture superficial statistical correlations. Furthermore, the absence of evidence-based clinical guidelines in representation learning leads to suboptimal predictive performance and inadequate reliability in safety-critical scenarios.To address these limitations, we propose the **G**uideline-**A**nchored **M**ultimodal Fusion with **P**rototype Mining (**GAMP**) framework, which leverages medical guidelines to efficiently extract clinically meaningful cross-modal temporal features from multimodal EHRs. Specifically, GAMP initializes shared prototype representations using prior knowledge derived from medical guidelines. We introduce a guideline-anchored alignment loss, enabling the model to comprehensively learn cross-modal temporal semantics while constraining the latent space distance between the learned representations and the clinical guideline knowledge. Finally, a dual-branch Transformer decoder is employed to fuse standardized medical evidence with individual patient characteristics, yielding predictions that are both accurate and clinically plausible. Our experiments on MIMIC-III for 48h in-hospital mortality prediction and 24h phenotype classification verify that our method surpasses current state-of-the-art baseline models.