使用PAJO深度学习模型对临床实践指南进行自动文献选
Yucong Lin1, Jia Li2, Huan Xiao3
1School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China.
BMC medical informatics and decision making
|November 4, 2023
概括
我们开发了PAJO模型,这是一种用于自动选文献的AI系统. 该工具有效地识别高质量的研究文章,大大改善了临床实践指南的制定.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 图书统计学 图书统计学
背景情况:
- 临床实践指南 (CPG) 依赖于高质量的研究,但手册文献选效率低下.
- 现有的人工智能 (AI) 方法缺乏有效的文献选能力.
- 开发自动化文献选对于推进CPG开发至关重要.
研究的目的:
- 提出论文标题,摘要和期刊 (PAJO) 模型,这是一个基于人工智能的系统,用于自动选文献.
- 解决手动和现有的基于人工智能的文献选方法的局限性.
- 提高对CPGs相关研究的识别效率和准确性.
主要方法:
- PAJO模型将文章选作为分类任务,使用CPG中的文章作为正样本.
- 它利用文章标题,摘要和期刊特征的特征,通过预训练的双向编码器-转换器 (BERT) 模型.
- 一个注意力机制集成文本和日志编码器进行最终分类.
主要成果:
- 该PAJO模型是通过PubMed的89940篇部疼痛文章的数据集进行训练和评估的.
- 它在F1得分方面表现1.91%高于最先进的基线,在ROC曲线下的面积方面表现2.25%高.
- 专家评估证实了PAJO成功选高质量的文章的能力.
结论:
- PAJO模型为自动化文献选提供了一个有效的解决方案.
- 它可以显著提高开发CPG的效率,从部疼痛研究开始.
- PAJO的方法适用于其他疾病领域的文献选.
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