自然语言处理用于增强的临床决策支持,用于药物处方的过敏验证
Juan Pablo Botero-Aguirre1, Michael Andrés García-Rivera2
1Department of Artificial Intelligence, Hospital Pablo Tobón Uribe, Colombia.
Mayo Clinic proceedings. Digital health
|December 15, 2025
概括
一个新的基于BERT的命名实体识别 (NER) 模型有效地从西班牙电子健康记录中提取过敏信息,通过检测处方错误来帮助药物安全.
科学领域:
- 自然语言处理自然语言处理.
- 临床信息学 临床信息学
- 药物监督 药物监督 药物监督
背景情况:
- 电子健康记录 (EHR) 包含有价值的患者数据,但通常是无结构的.
- 从西班牙电子健康记录中提取特定信息,如过敏细节,是一项挑战.
- 需要自动化方法来改善数据提取和患者安全.
研究的目的:
- 开发和验证基于BERT的命名实体识别 (NER) 模型,用于从西班牙电子健康记录中提取过敏信息.
- 评估模型在识别药物名称,不良反应和处方错误方面的表现.
- 评估该模型在临床决策支持中对提高药物安全性的有用性.
主要方法:
- 基于BERT的NER模型对来自西班牙EHR的16176个手动注释的过敏相关实体进行了微调.
- 数据集被分为培训 (80%) 和测试 (20%) 的子集.
- 模型性能使用准确性,回忆力,F1得分,灵敏度,特异性和Cohen κ进行评估,专家评价是黄金标准.
主要成果:
- 该模型获得了整体F1得分0.80,药物名称 (F1=0.91) 和不良反应 (F1=0.85) 的高性能.
- 它在识别非过敏病例方面表现出高特异性 (99.98%),并在0.96%的病例中检测到处方错误.
- 该模型显示了与专家注释的实质一致 (加权 κ=0.7797).
结论:
- 基于西班牙语BERT的NER模型在提取与过敏相关的信息和识别非过敏病例方面表现强.
- 该模型对临床决策支持具有前景,特别是在提高药物安全性方面,尽管敏感度中等.
- 这项研究表明,使用NER模型来提高西班牙语医疗保健机构的药物安全性的可行性.
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