利用自然语言处理来识别国家癌症研究所指定癌症中心的癌症相关出版物
Whitney Shae1, Md Saiful Islam Saif1, John Fife2
1Department of Biostatistics & Data Science, The University of Kansas Medical Center, Kansas City, KS 66160, United States.
JAMIA open
|December 15, 2025
概括
自然语言处理 (NLP) 方法可以自动化癌症出版物查. 在精选的癌症中心数据上训练的模型显示了更广泛的癌症相关性预测的前景.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 自然语言处理自然语言处理.
背景情况:
- 准确识别与癌症相关的出版物对于研究至关重要.
- 手动查是耗时和资源密集的.
- 使用NLP的自动化方法可以提高效率.
研究的目的:
- 在出版物中开发和评估NLP方法来查和预测癌症的相关性.
- 为了比较不同的文本编码和机器学习方法.
主要方法:
- 利用了两个数据集:精选的癌症中心出版物和癌症/非癌症摘要的衍生数据集.
- 探索了术语频率-反向文档频率 (TF-IDF) 和文本表示的BERT嵌入.
- 应用支向量分类 (SVC),梯度增强和多层感知子 (MLP) 分类器.
主要成果:
- 机器学习模型在衍生数据集中表现最好.
- SVC和MLP获得了高的F1分数 (分别高达0.976和0.997).
- 生物BERT嵌入式的性能一般优于TF-IDF.
结论:
- NLP有效地自动化了与癌症相关的出版物分类,提高了研究生产力.
- 仔细选择数据,文本表示和机器学习模型是必不可少的.
- 在精选数据上训练的模型显示了更广泛的癌症相关性预测的潜力.
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