通过多输入多输出序列标记模型分析临床试验对队列查询的资格标准
Shubo Tian1, Pengfei Yin2, Hansi Zhang2
1Department of Statistics, Florida State University, Tallahassee, USA.
概括
使用自然语言处理 (NLP) 将临床试验资格标准翻译成可计算的格式对于电子患者查至关重要. 一个监督的多输入多输出 (MIMO) 模型显示出潜力,但在解析这些标准时实现了低于最佳的性能.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算语言学 计算语言学
- 临床试验管理 临床试验管理
背景情况:
- 临床试验招聘依赖于复杂的资格标准,通常用非结构化的自由文本书写.
- 自动化解释这些标准对于有效的患者查和试验招生至关重要.
研究的目的:
- 调查使用监督的多输入多输出 (MIMO) 序列标记模型来分析临床试验资格标准.
- 评估模型将自由文本标准转化为可计算格式的能力.
主要方法:
- 采用了一个监督的多输入多输出 (MIMO) 序列标签模型.
- 在MIMO框架内使用了基于BERT的编码器.
- 实验是在一个小的,手动注释的训练数据集上进行的.
主要成果:
- 在MIMO框架下,总体上实现了0.61.6的宽松级AUROC.
- 性能不足于最佳,这表明解析精度有改进的余地.
- 该模型展示了将资格标准表示为逻辑和语义清晰的元组的潜力.
结论:
- 将临床试验资格标准表示为结构化组组是数据库查询翻译的一个有希望的方法.
- 需要进一步开发NLP模型,以提高自动化标准分析的准确性和可靠性.
- 这种方法可以提高临床试验电子患者查的效率.
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