论文研究 arXiv cs.CL Tue, 27 Oct 2026 正文 ok 261 words

面向药物警戒中个例安全报告的多智能体检索增强因果关系评估(MARCA)

英文标题:Multi-Agent Retrieval-Augmented Causality Assessment (MARCA) of Individual Case Safety Reports in Pharmacovigilance

研究团队提出了 MARCA(多智能体检索增强因果关系评估)框架,利用 Gemini 2.5 Pro 和 DeepSeek-V3.2 结合四智能体协作与 RAG 技术,辅助开展基于 Naranjo 算法的药物不良反应因果关系评估。在涵盖 215 份个例安全报告(ICSR)和 1,562 组药物-事件组合(DEC)的专家基准测试中,MARCA 与人类专家的最终因果分类一致率达 79.4%(Cohen's kappa = 0.587),且 RAG 在关键问题上的一致性显著高于非 RAG 方案。该研究验证了多智能体与检索增强技术在处理高通量医疗安全报告中的可行性与临床辅助潜力,为自动化临床药学推理与药物警戒系统的工程化落地提供了重要参考。
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中文

[提交于 2026 年 10 月 23 日]

标题:面向药物警戒中个例安全报告的多智能体检索增强因果关系评估(MARCA)

查看 Daisy Rae Lopez 等 3 位作者题为《面向药物警戒中个例安全报告的多智能体检索增强因果关系评估(MARCA)》的论文 PDF

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摘要:引言:个例安全报告(ICSR)数量的激增,对药物警戒中由专家主导的因果关系评估构成了巨大挑战。我们开发了 MARCA(多智能体检索增强因果关系评估,Multi-Agent Retrieval-Augmented Causality Assessment),这是一个用于基于 Naranjo 量表进行评估的四智能体框架,并评估了其与人类专家的一致性,以及非 RAG 与带 RAG 标注评分之间的探索性差异。
方法:MARCA 结合了科学文献检索、药品说明书评估、客观证据分类,以及使用 Gemini 2.5 Pro 和 DeepSeek-V3.2 进行的临床因果推理。智能体生成的合理性依据为人工辅助评分提供了参考。专家共识参考标准涵盖了来自 FAERS(2025 年第一季度至第二季度)的 215 份 ICSR 和 1,562 组药物-事件组合(DEC)。一致性通过百分比一致率和 Cohen's kappa 系数进行衡量。
结果:在问题层面上,Naranjo Q2 的一致率为 96.8%(kappa = 0.491;95% CI 0.265-0.663),Q5 为 85.4%(kappa = 0.269;95% CI 0.180-0.340),Q10 为 96.8%(kappa = 0.922;95% CI 0.885-0.951)。最终因果分类一致率为 79.4%(95% CI 71.8-85.3;kappa = 0.587,95% CI 0.450-0.690)。在包含 60 组 DEC 的回顾性子集中,带 RAG 标注的评分在 Q5(83.3% 对比 65.0%)和 Q10(100.0% 对比 18.3%)上的一致率均显著高于非 RAG 评分。
讨论:MARCA 辅助评估在各个 Naranjo 问题间展现出显著的一致性差异,其中对于替代原因的分析一致性较低。回顾性 RAG 对比属于探索性研究,尚未确立前瞻性有效性。

提交历史

来自:Maurizio Sessa 博士 [

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[v1]

2026 年 10 月 23 日 星期五 20:43:29 UTC (2,619 KB)

English / 原文

[Submitted on 23 Oct 2026]

Title:Multi-Agent Retrieval-Augmented Causality Assessment (MARCA) of Individual Case Safety Reports in Pharmacovigilance

View a PDF of the paper titled Multi-Agent Retrieval-Augmented Causality Assessment (MARCA) of Individual Case Safety Reports in Pharmacovigilance, by Daisy Rae Lopez and 2 other authors

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Abstract:Introduction: Increasing volumes of individual case safety reports (ICSRs) challenge expert-led causality assessment in pharmacovigilance. We developed MARCA (Multi-Agent Retrieval-Augmented Causality Assessment), a four-agent framework for Naranjo-based assessment, and evaluated agreement with human experts and exploratory differences between non-RAG and RAG-labelled scores.
Methods: MARCA combined scientific literature retrieval, drug-label assessment, objective-evidence classification, and clinical causality reasoning using Gemini 2.5 Pro and DeepSeek-V3.2. Agent-generated rationales informed human-assisted scoring. A consensus expert reference standard covered 215 ICSRs and 1,562 drug-event combinations (DECs) from FAERS (Q1-Q2 2025). Agreement was measured using percentage agreement and Cohen's kappa.
Results: Question-level agreement was 96.8% for Naranjo Q2 (kappa = 0.491; 95% CI 0.265-0.663), 85.4% for Q5 (kappa = 0.269; 95% CI 0.180-0.340), and 96.8% for Q10 (kappa = 0.922; 95% CI 0.885-0.951). Final causality classification agreement was 79.4% (95% CI 71.8-85.3; kappa = 0.587, 95% CI 0.450-0.690). In a retrospective subset of 60 DECs, agreement was higher for RAG-labelled than non-RAG scores for Q5 (83.3% vs 65.0%) and Q10 (100.0% vs 18.3%).
Discussion: MARCA-assisted assessment showed substantial variation in agreement across Naranjo questions, with lower concordance for alternative causes. Retrospective RAG comparisons are exploratory and do not establish prospective effectiveness.

Submission history

From: Maurizio Sessa Dr. [

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[v1]

Fri, 23 Oct 2026 20:43:29 UTC (2,619 KB)