从随机对照试验摘要中精确地提取PICO,使用特定部分的学习方法
Yan Hu1, Vipina K Keloth2, Kalpana Raja2
1School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77054, United States.
Bioinformatics (Oxford, England)
|September 5, 2023
概括
本研究介绍了一种两步自然语言处理 (NLP) 管道,用于从随机对照试验摘要中提取参与者,干预,比较和结果 (PICO). 该方法显著提高了用于证据合成的PICO元素提取精度.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
背景情况:
- 从随机对照试验 (RCT) 摘要中自动提取PICO (参与者,干预,比较,结果) 元素对于有效的证据合成至关重要.
- 以前用于PICO提取的自然语言处理 (NLP) 方法由于RCT摘要的复杂性和注释挑战而面临限制.
研究的目的:
- 开发和评估一个强大的两步NLP管道,用于从RCT摘要中准确地提取PICO元素.
- 通过改进自动化PICO信息检索来提高证据综合的效率和有效性.
主要方法:
- 提出了两步NLP管道,涉及使用基于提示的学习模型进行句子分类,然后使用命名实体识别 (NER) 模型进行PICO提取.
- 句子被分类为背景,方法,结果和结论,PICO提取集中在标题和方法部分,其中包含超过96%的PICO信息.
- 该管道在三个数据集上进行了评估:EBM-NLPmod,COVID-19 RCT摘要和阿尔茨海默病 (AD) RCT摘要.
主要成果:
- 在数据集中,NLP管道实现了高性能,代币级微F1得分为0.833 (EBM-NLPmod),0.928 (COVID-19),0.899 (AD).
- 实体级别的微型F1分数也很强,达到0.712 (EBM-NLPmod),0.850 (COVID-19),和0.805 (AD). 实体级别的微型F1分数也很强,达到0.712 (EBM-NLPmod),0.850 (COVID-19),和0.805 (AD). 实体级别的微型F1分数也很强,达到0.712 (EBM-NLPmod),0.850 (COVID-19),和0.805 (AD).
- 与以前的方法相比,拟议的方法在PICO提取精度方面取得了显著的改进.
结论:
- 开发的两步NLP管道有效地从RCT摘要中提取PICO元素,为证据合成提供了有价值的工具.
- 该方法在不同的医学领域显示出高准确性和通用性,包括传染病和神经退行性疾病.
- 公共可用的代码和数据集有助于进一步研究和应用这种PICO提取方法.
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