Autoregressive transformer modeling with missingness-aware contrastive pre-training outperforms baselines on MIMIC-IV and eICU benchmarks and mitigates divergent behavior from removed modalities in clinical trajectories.
URL https: //www.medrxiv.org/content/early/2024/10/22/2023.06.01.23290824
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2representative citing papers
Autoregressive LLM decoders with a missingness-aware contrastive pre-training objective outperform static baselines on MIMIC-IV/eICU and reveal demographic-bias failure modes under modality ablation.
citing papers explorer
-
Handling and Interpreting Missing Modalities in Patient Clinical Trajectories via Autoregressive Sequence Modeling
Autoregressive transformer modeling with missingness-aware contrastive pre-training outperforms baselines on MIMIC-IV and eICU benchmarks and mitigates divergent behavior from removed modalities in clinical trajectories.