一个基于"PubMedBERT + TextRNN"的多标签文本分类器,用于癌症文献
Zhang Ying1, Xia Guanghui1, Li Xiaoying1
1Institute of Medical Information, Chinese Academy of Medical Sciences, China.
Studies in health technology and informatics
|August 23, 2024
概括
这项研究开发了癌症研究文献的高分辨率多标签分类器,改善了文章选,并支持癌症数据不断增长的领域中准确的学术统计数据.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 癌症研究 癌症研究
背景情况:
- 癌症研究数据的指数增长需要有效的方法来分类文献.
- 高分辨率分类对于跨学科的研究和减少手工查负担至关重要.
研究的目的:
- 在出版层面开发和评估癌症研究文献的可扩展多标签分类器.
- 提高检索临床相关癌症出版物的准确性和效率.
主要方法:
- 一批癌症研究文献被分为培训 (70%) 和测试 (30%) 集.
- 进行了"PubMedBERT + TextRNN"和"BioBERT + TextRNN"分类器的性能比较.
- 根据精度,回忆和F1分数选择了最佳分类器.
主要成果:
- "PubMedBERT + TextRNN"分类器实现了高性能指标:精度 (P) = 0.952014,回忆 (R) = 0.936696,F1分数 (F1) = 0.931664. 这就是它的性能.
- 这证明了所选择的细粒度分类模型的有效性.
- 该分类器被证明适用于癌症文献的高分辨率分类.
结论:
- 开发的"PubMedBERT + TextRNN"多标签分类器为癌症研究文献的分类提供了一个强大的解决方案.
- 它有效地支持准确的文献检索和学术统计.
- 这一进步有助于研究人员在不断扩大的癌症数据中进行导航.
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