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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
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对基于主题的生物医学文献分类分类分类技术的比较分析.

Ihor Stepanov1,2, Arsentii Ivasiuk1,3, Oleksandr Yavorskyi4

  • 1Knowledgator Engineering Ltd., London, United Kingdom.

Frontiers in genetics
|November 29, 2023
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概括

简单的统计模型优于复杂的变压器,用于生物医学文本中对药物诱导肝损伤 (DILI) 的分类,特别是在不平衡的数据中. 这些方法提供更快,可解释的结果,对药物安全至关重要.

关键词:
迪利 迪利 迪利 是一个词.这是LSTM的LSTM.生物医学文献分类生物医学文献分类信息理论信息理论机器学习是机器学习.文本采矿 文本采矿是什么基于变压器的方法不平衡的数据不平衡的数据.

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科学领域:

  • 生物医学信息学 生物医学信息学
  • 计算语言学 计算语言学
  • 药物监督 药物监督 药物监督

背景情况:

  • 科学文献的数量增长挑战了信息提取.
  • 准确识别药物诱导性肝损伤 (DILI) 对患者安全和药物开发至关重要.
  • 需要自动分类方法来有效处理生物医学文本以获取DILI信息.

研究的目的:

  • 为了比较各种文本分类方法来识别科学文章中的DILI信息.
  • 开发一种快速,可解释的文本分类方法,用于特定领域的分类.
  • 为了解决生物医学文本分类中的数据不平衡挑战.

主要方法:

  • 变压器,LSTM,信息理论和基于统计的文本分类方法的比较.
  • 开发一种新的,可解释的文本分类模型.
  • 实施处理不平衡数据集的技术.

主要成果:

  • 变压器在训练和测试数据分布匹配时表现最好.
  • 简单的统计和信息理论模型在不平衡的数据上超过了复杂的变压器,提供了更好的解释性.
  • 特定领域的预训练和量身定制的损失功能改善了神经网络对不平衡的生物医学数据的性能.

结论:

  • 虽然变压器是强大的,但更简单的统计方法可以更有效和可解释的生物医学主题分类.
  • 结合复杂和简单模型的优势,为未来的研究提供了有前途的途径.
  • 需要进一步开发神经网络架构和培训策略,以便对不平衡的生物医学数据进行强有力的分类.