在出版层面的癌症文献的多标签文本分类器:医学文本分类方法研究的方法
Ying Zhang1, Xiaoying Li1, Yi Liu1
1Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China.
JMIR medical informatics
|October 5, 2023
概括
一个新的多标签分类模型,BERT + TextRNN,通过将多个标签分配给出版物来增强癌症文献检索. 这种高分辨率的方法提高了临床相关性和研究分析的准确性.
科学领域:
- 计算生物学和生物信息学
- 医疗信息学和数据科学
背景情况:
- 癌症研究数据的快速增长需要先进的分类方法,而不仅仅是期刊级别的分析.
- 现有的单一标签分类系统不足以捕捉癌症研究文献的跨学科性质.
- 需要一个高分辨率的多标签分类器来改善文献检索和临床相关性查.
研究的目的:
- 为癌症研究出版物开发一个高分辨率的多标签分类器.
- 提高临床和研究目的的文献检索的准确性和全面性.
- 为了解决当前期刊级分类系统的局限性.
主要方法:
- 使用了70,599份癌症出版物的库,分为培训 (70%) 和测试 (30%) 集.
- 使用变压器 (BERT) + X 模型架构的双向编码器表示方法开发了一个多标签分类器.
- 通过将BERT与五种经典的深度学习模型 (例如TextRNN,FastText) 结合使用国际癌症研究伙伴关系术语来评估性能.
主要成果:
- 确定的最佳模型组合是"BERT + TextRNN",达到93.09%的精度,87.75%的回忆率和90.34%的F1分数.
- 该模型证明了在出版物层面有效的多标签分类.
- 量化了文本结构和多标签分布特征,以便在其他领域进行潜在的概括.
结论:
- "BERT + TextRNN"模型为癌症文献提供了高分辨率的分类,增强了检索和学术统计数据.
- 该模型自动为每个癌症论文分配一个或多个相关标签.
- "BERT + TextRNN"模型在癌症文献的多标签分类方面表现优于其他经过测试的模型,具有更广泛应用的潜力.
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