使用风险控制预测校准来降低发作预测中的错误警报率
Galya Segal1,2, Noam Keidar1, Roy Maor Lotan3,4
1Laboratory of Bioenergetic and Bioelectric Systems, Biomedical Engineering Faculty, Technion-Israel Institute of Technology (IIT), Haifa, Israel.
Frontiers in neuroscience
|October 4, 2023
概括
一种新的校准方法显著减少了发作预测算法中的错误警报. 这一进步旨在使实时预测系统在临床使用中更可靠.
科学领域:
- 神经学 神经学
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 是一种神经系统疾病,导致不可预测的发作,严重影响患者的生活质量.
- 目前的预测算法受到高错误报警率的阻碍,限制了它们的临床适用性.
- 通过准确的预测及时干预可以改善患者的治疗结果.
研究的目的:
- 为了引入和验证一种新的校准方法,学习然后测试,以减少预测中的错误报警率.
- 在患者的脑电图 (EEG) 数据上调整和测试这种方法.
主要方法:
- 应用了学习然后测试校准方法来调整深度学习预测模型的输出.
- 最初的验证是使用合成数据进行的.
- 该方法随后在15名患者的公开可用的EEG记录上进行了测试.
主要成果:
- 校准方法有效地控制了虚假报警率到用户指定的水平.
- 在实体患者脑电图数据上观察到,错误报警的平均减少为92%.
- 这种方法导致六名患者的九次发作中有四次错过.
结论:
- "学习然后测试"校准方法在减少发作预测错误警报方面显示出有前途.
- 这种方法可以提高实时预测系统的临床实用性.
- 进一步整合先进的预测模型可能会导致更强大的临床工具.
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