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通过模型性能估计数据质量:机器学习作为验证工具
Gleb Danilov1, Konstantin Kotik1, Michael Shifrin1
1Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation.
Studies in health technology and informatics
|June 30, 2023
概括
重新定义神经外科手术报告的目标变量显著改善了深度学习分类准确性. 这种增强的方法实现了99.5%的准确性,优化了用于医学文本分析的机器学习.
科学领域:
- 神经外科 神经外科
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 分类神经外科手术报告对于数据分析至关重要.
- 以前的专家衍生分类方法实现了低于最佳的F分数 (≤0.74).
- 在自动化医学文本分类中,需要提高准确性是显而易见的.
研究的目的:
- 通过使用深度学习来增强神经外科手术报告的短文本分类.
- 调查改进目标变量的对分类器性能影响.
- 将深度学习模型应用于真实世界的神经外科数据.
主要方法:
- 根据病理,局部和操纵类型重新设计目标变量.
- 实施深度学习模型用于文本分类.
- 在真实数据集上使用准确度和F1得分来评估模型性能.
主要成果:
- 重新设计的目标变量显著改善了深度学习模型的性能.
- 获得了0.995的分类准确度和0.990.990.1的F1分数.
- 成功将作战报告分为13个不同的类别.
结论:
- 改进目标变量定义是有效的基于机器学习的文本分类的关键.
- 深度学习模型,当被明确的目标指导时,可以在医疗报告分析中实现高精度.
- 机器学习可以作为验证人类生成编码的工具.
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