用大模型辅助医生更快更准地识别皮肤免疫副作用
Human-in-the-Loop Large Language Model Framework for Identification of Cutaneous Immune-Related Adverse Events
- 构建人机协作的多智能体大模型框架，结合检索增强技术
- 检测准确率提升至F1=0.88，评阅时间减半，一致性显著提高
- 适合临床研究、药物安全监测人员快速提取不良反应数据
本研究评估了一种检索增强的多智能体大语言模型（LLM）驱动的人机协同框架，用于从临床记录中识别皮肤免疫相关不良事件（cirAEs）。与人工独立审阅相比，该框架在准确率（F1=0.88 vs 0.77）、评分者间一致性（Cohen's kappa=0.82 vs 0.50）方面均有提升，并将平均审阅时间减少约一半。该框架为大模型在跨器官系统免疫毒性识别中的应用提供了范例，更广泛地支持了不良事件数据的精准、可扩展且透明的提取。
原文摘要 · Abstract (English)
This study evaluated a retrieval-augmented, multi-agent large language model (LLM)-driven, human-in-the-loop framework for detecting cutaneous immune-related adverse events (cirAEs) from clinical notes. Compared with unassisted manual review, the LLM-assisted workflow improved accuracy (F1 = 0.88 vs 0.77), inter-rater agreement measured by Cohen's kappa (kappa = 0.82 vs 0.50), and reduced average review time by approximately half. This framework pilots how LLMs can be applied to identify immune-related toxicities across organ systems and, more broadly, enable accurate, scalable, and transparent adverse event data extraction.
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