基于变压器的大型语言模型在识别来自新加坡非结构化出院摘要的不良药反应关系方面的有效性
Yen Ling Koon1, Yan Tung Lam1, Hui Xing Tan1
1Vigilance and Compliance Branch, Health Products Regulation Group, Health Sciences Authority, 11 Biopolis Way, #11-01 Helios, Singapore, 138667, Singapore.
Drug safety
|February 21, 2025
概括
基于变压器的大型语言模型 (LLM) 显著改善了临床笔记中药物不良事件 (AE) 对的识别. 基于规则的进一步改进提高了精度,尽管LLM在因果关系检测方面仍然存在一些局限性.
科学领域:
- 自然语言处理自然语言处理.
- 生物医学信息学 生物医学信息学
- 临床数据分析 临床数据分析
背景情况:
- 大型语言模型 (LLM) 具有先进的文本处理,但在非标准的临床语言方面面临挑战.
- 在非结构化的临床文本中识别药物不良事件 (AE) 对对患者安全至关重要.
研究的目的:
- 评估基于变压器的LLM在识别因果相关药物-AE对中的有效性.
- 将LLM的表现与传统的机器学习模型进行比较.
- 评估LLM在不同临床数据集中的通用性.
主要方法:
- 微调9个基于变压器的LLM在5088个注释的药物-AE对上.
- 验证了新加坡排放摘要中的1647个段落和MIMIC-III数据库中的4418个段落的LLM.
- 将LLM绩效 (F1分数) 与传统机器学习模型的基准进行比较.
主要成果:
- 基于变压器的LLMs,特别是BioM-ELECTRA-Large-BiLSTM,与之前的基准相比,F1得分平均提高了16.1% (从0.64到0.74).
- 一个LLM-双向长短期内存 (LLM-BiLSTM) 架构显示了性能的提高.
- 应用额外的规则降低了假阳性率,提高了高达5.6%的精度.
结论:
- 基于变压器的LLM在释放总结中显示出高于传统方法的性能,用于检测因果药物-AE关系.
- 基于规则的LLM预测的后处理进一步完善了准确性,表明了LLM对此任务能力的持续改进领域.
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