药物相关事故因素自动分类系统的开发:改善患者安全管理的实用方法
Yuri Takamatsu1, Sayaka Ebara1, Hayato Kizaki1
1Division of Drug Informatics, Keio University Faculty of Pharmacy.
Studies in health technology and informatics
|August 8, 2025
概括
使用自然语言处理 (NLP) 对药物事件报告的自动分析有效地识别了导致因素. 这种方法支持改善患者安全和药物错误管理.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 患者安全 患者安全
背景情况:
- 分析与药物相关的事故报告对于患者安全至关重要.
- 从这些报告中系统地提取促成因素是一个重大挑战.
研究的目的:
- 开发和评估一个多标签分类器,用于在与毒品有关的报告中自动识别事件因素.
- 评估NLP技术的可行性,用于系统的事件因子分析.
主要方法:
- 利用来自变压器的双向编码器表示 (BERT) 和其衍生品进行分类.
- 在1,212个与毒品有关的事件报告中开发了多标签分类器.
- 采用了P-mSHELL模型框架,并使用五倍交叉验证评估模型.
主要成果:
- 多标签分类器成功地在七个不同的类别中识别了事件因素.
- 大多数评估的模型都取得了超过0.6.6的宏观F1分数.
- 一个简单的BERT显示了与BERT相比的性能.
结论:
- 对于系统的事件因子分析,自然语言处理技术实际上是可行的.
- 这种方法可以支持加强患者安全管理,减少药物错误.
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