在手术中自动定位发性区域的阶级失衡问题:系统性审查
Valentina Hrtonova1,2,3, Kassem Jaber3,4, Petr Nejedly1,2
1First Department of Neurology, Faculty of Medicine, Masaryk University, Brno, Czech Republic.
Journal of neural engineering
|June 9, 2025
概括
内脑电图 (iEEG) 数据中的类失衡使得手术的自动发性区域 (EZ) 定位变得复杂. 解决这种数据不平衡对于提高EZ本地化中使用的机器学习模型的可靠性和性能至关重要.
科学领域:
- 神经科学是一个神经科学.
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 精确地定位发性区域 (EZ) 对于成功的手术至关重要.
- 内脑电图 (iEEG) 数据提出了阶级不平衡的挑战,发性接触者比非发性接触者少.
- 自动EZ定位的机器学习 (ML) 方法受到这种数据不平衡的重大影响.
研究的目的:
- 在基于ML的EZ本地化研究中使用iEEG数据系统地审查和评估处理类不平衡的方法.
- 在EZ本地化背景下,确定共同的策略及其在解决数据不平衡方面的有效性.
- 突出阶级失衡对自动化EZ本地化模型可靠性和临床实用性的影响.
主要方法:
- 系统的文献综述使用ML进行EZ定位从iEEG数据的研究.
- 专注于解决数据处理,算法设计和评估指标中的阶级不平衡的方法.
- 在2,128篇选的出版物中,分析了35篇精选的研究.
主要成果:
- 源接触者占所有接触者中位数的18.34%在审查的研究中.
- 许多研究没有充分解决阶级不平衡问题.
- 采用了诸如数据重新采样和成本敏感学习等常见技术,但评估指标往往无法解释失衡.
结论:
- 类不平衡显著损害了基于ML的EZ本地化模型的可靠性.
- 需要更强大,更具创新性的方法来全面管理阶级不平衡.
- 改善处理类失衡是提高EZ本地化模型的预测性能和临床适用性至关重要的.
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