What Next-Event Accuracy Cannot See: Closed-Loop Evaluation of Emergency Department Trajectory Simulators

arXiv:2609.31635 · cs.LG, cs.AI · Submitted 2026-09-10 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-09-10

Updated: 2026-09-10

Code: https://github.com/zxKyouma/EDSim-Bench

Terminology

• Transformer — 127× in this paper · explained in 2 episodes
• Accuracy — 23× in this paper · explained in 1 episode
• Fidelity — 20× in this paper · explained in 2 episodes
• Benchmark — 12× in this paper · explained in 2 episodes
• Calibration — 10× in this paper · explained in 7 episodes
• AUC — 9× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• International Conference on Learning Representations — 4× in this paper
• Consistency — 3× in this paper · explained in 3 episodes
• Trace — 3× in this paper · explained in 2 episodes
• AUROC — 2× in this paper · explained in 2 episodes
• Exposure Bias — 2× in this paper · explained in 2 episodes
• World Model — 2× in this paper · explained in 7 episodes
• Benchmarking — 1× in this paper · explained in 4 episodes
• Core — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• EMMA — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Institutional Review Board — 1× in this paper
• Jensen–Shannon Divergence (JSD) — 1× in this paper · explained in 2 episodes
• Regret — 1× in this paper · explained in 3 episodes

Sources

• How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary?
• EHRWorld: A Patient-Centric Medical World Model for Long-Horizon Clinical Trajectories
• ChronoMedicalWorld: A Medical World Model for Learning Patient Trajectories from Longitudinal Care Data

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