了解医学文本到SQL模型和数据集的泛化
Richard Tarbell1, Kim-Kwang Raymond Choo2, Glenn Dietrich2
1Data Analytics, The University of Texas at San Antonio, USA.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|January 15, 2024
概括
为电子医疗记录 (EMR) 生成准确的文本到SQL仍然具有挑战性. 新的数据分割揭示了当前模型的显著性能下降,突出显示了医疗数据分析中需要提高概括性的需求.
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:
- 电子病历 (EMR) 是复杂的关系数据库.
- 访问EMR数据需要专门的数据库知识,这阻碍了研究.
- 文本到SQL旨在简化医疗保健专业人员的数据检索.
研究的目的:
- 重新评估文本到SQL模型在医疗领域的真正性能.
- 突出现有数据集在评估模型通用性方面的局限性.
- 引入用于改善EMR数据的文本到SQL模型稳定性的方法.
主要方法:
- 在MIMICSQL数据集中创建了新的,更具挑战性的数据分割.
- 在这些新分裂上评估了最先进的语言模型.
- 开发并应用了一种新的数据增强技术.
主要成果:
- 最先进的模型显示,在新数据分割时,准确度从90%以上下降到28%以下.
- 现有的数据集不能充分反映现实世界医疗文本到SQL任务的难度.
- 数据增强提高了模型的概括性.
结论:
- 目前的文本到SQL模型对于复杂的医疗EMR数据是不够普遍的.
- 需要进一步的研究来开发医疗保健的强大的文本到SQL解决方案.
- 这项工作为通过自然语言获得更可靠的医疗数据提供了基础.
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