Handling and Interpreting Missing Modalities in Patient Clinical Trajectories via Autoregressive Sequence Modeling

This work addresses the challenge of missing modalities in multimodal clinical data for diagnosis by reframing it as an autoregressive sequence modeling task. It leverages causal decoders from LLMs and a missingness-aware contrastive pre-training to outperform baselines on benchmarks like MIMIC-IV and eICU.