Cell子刊：孔桂兰/马青变合作开发AI智能体系统，预测急诊重症患者的谵妄

TL;DR - Researchers developed DeLiriuMAgents, an LLM-driven multidisciplinary agent system for early delirium prediction in critically ill emergency patients. Its external validation across multiple cohorts suggests potential for transferable, interpretable clinical decision support.

• Combines machine-learning risk prediction, LLM-based emergency medicine, neurology, and psychiatry agents, plus RAG-sourced medical evidence.
• Uses MIMIC-IV for development and internal validation, with external validation on PKU multicenter and eICU-CRD cohorts.
• Accuracy/sensitivity/specificity were 0.749/0.762/0.747 on MIMIC-IV, 0.731/0.708/0.736 on PKU, and 0.670/0.708/0.665 on eICU-CRD.
• Chart review and clinician evaluation supported the generated risk reports’ interpretability and practical utility.