一个基于跨度的模型,用于从随机控制试验出版物中提取重叠的PICO实体.
Gongbo Zhang1, Yiliang Zhou2, Yan Hu3
1Department of Biomedical Informatics, Columbia University, New York, NY 10032, United States.
概括
PICOX是一种用于提取人口,干预,比较和结果 (PICO) 实体的新方法,显著提高了证据检索的准确性. 这种方法提高了在生物医学文献中识别复杂PICO元素的精度和回忆力.
科学领域:
- 自然语言处理自然语言处理.
- 生物医学信息学 生物医学信息学
- 信息检索 信息检索
背景情况:
- 提取PICO实体对于在系统性审查和临床研究中有效检索证据至关重要.
- 现有的方法难以识别重叠的PICO实体,限制了它们的有效性.
研究的目的:
- 介绍PICOX,一种用于提取重叠的PICO实体的新方法.
- 根据各种生物医学数据集的既定基线评估PICOX的性能.
主要方法:
- PICOX识别实体边界,并使用多标签分类器来分配PICO标签.
- 该方法在PICO-Corpus,阿尔茨海默病和COVID-19数据集上得到验证,使用实体级精度,回忆和F1分数.
- 进行了废弃性研究,以评估数据增强的影响.
主要成果:
- 与EBM-NLP基线相比,PICOX在所有测试的数据集中显示出更高的精度,回忆和F1分数.
- 微型F1得分显著改善,例如,从45.05到50.87整体和从77.10到80.32的COVID-19数据集.
- 在PICO-Corpus上,PICOX实现了更高的回忆和F1分数,在阿尔茨海默病数据集上,可比的F1分数获得了更高的精度.
结论:
- PICOX有效地提取重叠的PICO实体,优于领先的方法.
- 在PICOX中数据增强策略是最大限度地减少假阳性和提高精度的关键.
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