MedT5SQL:一个基于转换器的大型语言模型,用于医疗保健领域的文本到SQL转换
Alaa Marshan1, Anwar Nais Almutairi2, Athina Ioannou3
1School of Computer Science and Electronic Engineering, University of Surrey, Guildford, United Kingdom.
Frontiers in big data
|July 11, 2024
概括
医疗人员现在可以使用自然语言查询访问电子病历 (EMR). MedT5SQL模型将文本转换为SQL,为非技术用户改进EMR检索.
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.
- 数据库管理数据库管理
背景情况:
- 电子医疗记录 (EMR) 越来越普遍,这给医疗保健工作人员带来了有限的数据库专业知识的检索挑战.
- 可访问的EMR检索对于有效的医疗保健提供至关重要.
- 自然语言处理 (NLP) 通过文本到SQL转换提供了一个解决方案.
研究的目的:
- 评估现有的EMR检索文本到SQL方法.
- 提出和评估MedT5SQL模型,用于将自然语言查询转换为用于EMR访问的SQL查询.
主要方法:
- 基于Text-to-Text Transfer Transformer (T5) 大型语言模型 (LLM) 的MedT5SQL模型在MIMICSQL数据集上进行了微调.
- 使用两个优化器,不同的训练时代以及MIMICSQL和WikiSQL数据集来评估性能.
主要成果:
- 在MIMICSQL上,MedT5SQL实现了高精度:准确匹配率为80.63%,近似字符串匹配率为98.937%,手动评估率为90%.
- 在WikiSQL数据集上,MedT5SQL显示了44.2%的准确性和94.26%的近似字符串匹配.
结论:
- 精心调整的T5模型显示了将自然语言医学查询转换为SQL的巨大潜力.
- MedT5SQL模型提供了一种有效的解决方案,以改善非技术医疗人员的EMR检索.
- 这项研究为在医疗保健数据获取方面推进NLP应用提供了基础.
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