通过微调的文本嵌入模型来提高MedDRA/J编码精度
Shoya Wada1,2, Masaharu Okamoto2, Kento Sugimoto2
1Department of Transformative System for Medical Information, Graduate School of Medicine, The University of Osaka.
Studies in health technology and informatics
|August 8, 2025
概括
对不良事件 (AE) 数据的微调文本嵌入模型改善了日本的MedDRA编码精度. 这种方法提高了搜索精度和回忆,以更好地遵守法规.
科学领域:
- 药物监督 药物监督 药物监督
- 自然语言处理自然语言处理.
- 医学编码 医学编码
背景情况:
- 日本的MedDRA (监管活动医学词典) 编码存在挑战,原因是源表达式和词典术语之间的差异.
- 准确的不良事件 (AE) 报告对于患者安全和监管合规至关重要.
研究的目的:
- 提高日本MedDRA编码的准确性和效率.
- 使用内部AE数据开发和评估一个微调的文本嵌入模型.
主要方法:
- 一个文本嵌入模型在50,000个内部不良事件条目数据集上进行了微调.
- 微调模型的性能与MedDRA术语排名和回忆的基线方法进行了比较.
主要成果:
- 微调模型在MedDRA术语排名和回忆中显示出显著的改进.
- 实现了76.2%的nDCG@20和90.8%的RECALL@20与5000个内部条目.
- 在准确地将源表达式映射到MedDRA术语方面表现优于基线方法.
结论:
- 在现实世界AE数据上微调文本嵌入模型是改进MedDRA/J编码的可行策略.
- 这种先进的方法可以通过提高搜索准确性和效率,显著有利于药监活动.
- 该方法提供了一个可扩展的解决方案,用于解决监管环境中的编码差异.
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