PRISMATIC：多智能体协作降低多重病患者用药冲突
PRISMATIC : Prescription Risk Inspection System for Multi-Agent Treatment Interaction in Clinical Decision-Making
用多智能体分工来管多重病患者的复杂用药，靠 DSWA 和 DFPC 两套协调机制减少药物冲突，在 MIMIC-IV 数据上验证有效，做医疗 LLM 应用的可以看看。
PRISMATIC 是一个面向多重病患者的多智能体用药架构，每个 agent 扮演一个临床工作流角色，基于黑板架构协同生成和修正用药方案。协调机制包括 DSWA（根据历史病例结果长期调整各 agent 权重）和 DFPC（短期迭代反馈以消除当前患者的处方冲突）。在 MIMIC-IV 数据库患者记录上的实验显示，与强 LLM 基线相比，PRISMATIC 显著减少药物冲突、提高处方覆盖率并增强治疗一致性。
PRISMATIC : Prescription Risk Inspection System for Multi-Agent Treatment Interaction in Clinical Decision-Making
Medical prescribing errors for multimorbid patients, who require complex drug regimens, are a major patient safety concern. These errors often stem from fragmented decision-making across medical specialties, a critical coordination failure that leads to adverse drug events and rehospitalizations. To address this coordination challenge, we introduce PRISMATIC, a multi-agent architecture where specialized agents, each representing a workflow role, collaboratively generate and refine treatment plans. Operating on a blackboard architecture, the system first structures patient information from clinical notes, then iteratively develops safe, rational, and interpretable drug regimens. The agents' collaboration is governed by two novel adaptive coordination mechanisms: (1) Dynamic Self-Updating Weight Adjustment (DSWA), a long-term mechanism that adjusts the influence weights of agents based on historical case outcomes, enabling the system to adapt and trust more reliable specialists over time. (2) Discrepancy Feedback Precise Calibration (DFPC), a short-term mechanism where agents provide iterative feedback to resolve disagreements and eliminate prescription conflicts for the current patient. Experiments on patient records from the MIMIC-IV database show that PRISMATIC significantly reduces drug conflicts, improves prescription coverage, and enhances treatment consistency compared to strong LLM baselines. Our work demonstrates that structuring clinical co-treatment as a multi-agent coordination problem offers a promising path toward safer and more coherent multidisease management.
