icu

This paper presents VDSS, a human-in-the-loop multi-agent framework for ventilator decision support that uses contextual bandit preference learning to adapt to clinician-specific tuning styles, with retrospective ICU trajectory replays showing improved recommendation acceptability and reduced interaction rounds.

RealICU is a hindsight-annotated benchmark for evaluating LLMs in ICU settings, covering four physician-motivated tasks. Experiments reveal that existing LLMs struggle with recall-safety tradeoffs and anchoring bias, while a new structured-memory agent improves reasoning but not fully eliminate safety failures.

This paper introduces a stochastic causal representation learning framework to resolve the bias-precision paradox in personalized medicine, demonstrating improved accuracy and interpretability in ICU clinical decision support.