从非结构化的荷兰心声回声图报告中提取诊断,使用跨度和文档级别的特征分类.
Bauke Arends1, Melle Vessies2, Dirk van Osch2
1Department of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands. b.k.o.arends-4@umcutrecht.nl.
BMC medical informatics and decision making
|March 6, 2025
概括
从荷兰回声心电图报告中自动提取诊断是可行的,使用先进的机器学习模型,如SpanCategorizer和MedRoBERTa.nl. 这些工具大大减少了手工劳动,提高了临床研究和决策支持的效率.
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.
- 心脏病学 心脏病学
背景情况:
- 临床机器学习和人工智能决策支持需要准确的标签.
- 从临床报告中手动提取标签是耗时且昂贵的.
- 这项研究评估了从荷兰回声心脏图报告中提取的自动诊断.
研究的目的:
- 评估自动跨度和文档级诊断提取的可行性.
- 为了比较各种自动标签技术的性能.
- 确定临床报告分析的最佳模型.
主要方法:
- 利用了115,692份荷兰心声回声图报告.
- 手动注释心脏特征的一个子集.
- 开发并评估了跨度和文档级标签模型 (SpanCategorizer,MedRoBERTa.nl,SetFit).
- 使用权重和宏 F1 评分,精度和回忆来衡量性能.
主要成果:
- SpanCategorizer和MedRoBERTa.nl在分别对跨度和文档分类表现出了卓越的表现.
- 权重的F1分数在0.60-0.93 (SpanCategorizer) 和0.96-0.98 (MedRoBERTa.nl) 之间.
- 直接的文档分类表现优于间接的方法;SetFit在有限的数据中显示出有希望的结果.
结论:
- 建议SpanCategorizer和MedRoBERTa.nl用于从荷兰回声心脏图中提取诊断.
- 建议SetFit作为文件分类的替代方案,培训数据有限.
- 建议未来对基于RoBERTa的跨度分类器和英语模型翻译进行研究.
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