临床试验结果的自动表格化:一个联合实体和关系提取方法与基于变压器的语言表示
Jetsun Whitton1, Anthony Hunter1
1Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK.
Artificial intelligence in medicine
|October 2, 2023
概括
现在可以自动生成系统审查的证据表. 这项研究开发了深度学习模型,从随机对照试验中提取研究结果,显著提高了效率.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能在医学中的应用
- 生物医学信息学 生物医学信息学
背景情况:
- 基于证据的医学依赖于系统性审查来综合研究结果.
- 从随机对照试验 (RCT) 中手动创建证据表是耗时和劳动密集的.
- 自动化这个过程可以显著加速医学证据的合成.
研究的目的:
- 调查从RCT摘要生成证据表的自动化.
- 开发和评估深度神经网络模型,以提取研究结果的结果.
- 评估系统在各种NLP任务和疾病领域的性能.
主要方法:
- 使用深度神经网络将问题分解为命名实体识别和关系提取.
- 员工转移学习和基于变压器的语言表示用于模型开发.
- 创建了一个新的黄金标准语料库,包含来自六个疾病领域的550多个结果句子,用于培训和测试.
主要成果:
- 开发的系统在多个NLP任务和疾病领域表现出强的性能.
- 这些模型显示了对在训练中未遇到的疾病领域的概括能力.
- 只有170个示例句子被训练的模型取得了有效的结果.
结论:
- 证据表的生成可以半自动化,大大减少手工工作.
- 这种方法是朝着系统审查的完全自动化迈出的关键一步.
- 开发的模型提供了一种有效的方法,可以从RCT摘要中提取关键信息.
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