幻觉的大小和影响表格合成健康数据的预测机器学习模型:验证研究验证研究
Lisa Pilgram1,2,3, Samer El Kababji2, Dan Liu1,2
1School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada.
Journal of medical Internet research
|August 18, 2025
概括
幻觉在合成健康数据中很常见,但不会损害预后模型的性能. 这项研究表明,尽管数据不完美,合成数据仍然对机器学习任务有用.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 合成数据生成 (SDG) 的生成人工智能可以推进健康研究.
- 幻觉,在文本AI中已知的问题,也可以在表格SDG中发生.
- 了解这些幻觉对于可靠的健康数据创新至关重要.
研究的目的:
- 在表式合成健康数据中调查幻觉的频率.
- 确定幻觉频率是否与训练数据的复杂性相关.
- 评估幻觉对合成数据对预后机器学习 (ML) 模型有用性的影响.
主要方法:
- 从12个不同复杂度的现实世界健康数据集中生成了6354个合成数据集.
- 利用7种不同的SDG模型创建合成数据.
- 将幻觉定义为非群体记录,并测量幻觉率 (HR).
- 使用ML (光梯度增强机器) 和人工神经网络 (多层感知器) 评估下游预测性能.
主要成果:
- 幻觉发生率差异很大 (0.3%100%),中位数为99.1%,随着数据的复杂性而增加.
- 尽管幻觉率很高,但大多数SDG模型对ML预后模型的性能没有显著影响.
- 在检测到关联的模型中观察到最小的性能下降 (最大AUC变化为-0.0002).
结论:
- 幻觉在合成表格健康数据中很普遍.
- 高的幻觉率并不一定会影响合成数据对预后建模的有用性.
- 合成健康数据仍然是开发预测ML模型的宝贵资源.
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