TrajOnco uses a chain-of-agents LLM architecture with memory to perform temporal reasoning on longitudinal EHR, achieving 0.64-0.80 AUROC for 1-year multi-cancer risk prediction in zero-shot mode on matched cohorts while matching supervised ML on lung cancer and outperforming single-agent baselines.
and Was, Jaroslaw and Li, Quanzheng and Bates, David W
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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.
DT-Transformer predicts next disease events with median age- and sex-stratified AUC 0.871 across 896 categories on held-out and prospective data from a 1.7M-patient multi-hospital EHR dataset.
citing papers explorer
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TrajOnco: a multi-agent framework for temporal reasoning over longitudinal EHR for multi-cancer early detection
TrajOnco uses a chain-of-agents LLM architecture with memory to perform temporal reasoning on longitudinal EHR, achieving 0.64-0.80 AUROC for 1-year multi-cancer risk prediction in zero-shot mode on matched cohorts while matching supervised ML on lung cancer and outperforming single-agent baselines.
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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.
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DT-Transformer: A Foundation Model for Disease Trajectory Prediction on a Real-world Health System
DT-Transformer predicts next disease events with median age- and sex-stratified AUC 0.871 across 896 categories on held-out and prospective data from a 1.7M-patient multi-hospital EHR dataset.