在随机对照试验出版物中自动分类自我承认的限制
Mengfei Lan1, Mandy Cheng2, Linh Hoang1
1School of Information Sciences, University of Illinois Urbana-Champaign, 501 Daniel Street, Champaign, 61820, IL, USA.
Journal of biomedical informatics
|March 28, 2024
概括
研究人员开发了自然语言处理 (NLP) 方法,在随机对照试验 (RCT) 中自动检测和分类研究局限性. 该工具通过识别出版物中的报告问题来提高科学透明度.
科学领域:
- 医疗信息学 医疗信息学
- 临床试验 研究 研究 临床试验 临床试验
- 自然语言处理自然语言处理.
背景情况:
- 承认研究的局限性对于科学透明度和进步至关重要.
- 目前科学出版物中关于限制的报道往往是不够的.
- 自然语言处理 (NLP) 提供了自动化检查的潜力,以提高研究透明度.
研究的目的:
- 开发一个数据集和NLP方法来检测和分类随机对照试验 (RCT) 出版物中自我承认的局限性.
- 提高识别和分类研究局限性的准确性和效率.
- 为了能够对临床研究中的限制报告进行大规模分析.
主要方法:
- 为限制类型创建了一个数据模型,包含15个类别和24个子类别.
- 在200个RCT全文出版物中注释了1090个限制实例.
- 微调的基于BERT的模型 (PubMedBERT) 用于句子和类型的分类,包含数据增强 (EDA,PromDA).
- 将表现最好的模型应用于约12,000个RCT出版物.
主要成果:
- 限制性句子分类的PubMedBERT模型获得了F1得分0.821.
- 性能最好的限制类型分类模型 (PubMedBERT与PromDA) 的F1得分为0.7,提高了2.7个百分点.
- 通过微调和数据增强技术观察到分类准确性的显著改善.
结论:
- 开发的NLP模型可以支持期刊的自动选工具来识别报告问题.
- 自动提取限制可以增强同行评审和证据综合.
- 这种方法有助于从临床试验文献中更好地搜索和汇总证据.
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