利用VQ-VAE代币化用于医学时间序列的自回归建模
Yoonhyung Lee1, Younhyung Chae1, Kyomin Jung2
1Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
Artificial intelligence in medicine
|July 5, 2024
概括
CodeAR使用一种新的生成模型合成电子健康记录 (EHR) 数据. 这种方法增强了患者的隐私,并提高了医疗时间序列建模的准确性.
科学领域:
- 人工智能的人工智能
- 生物医学信息学 生物医学信息学
- 机器学习 机器学习
背景情况:
- 电子健康记录 (EHR) 包含敏感的患者信息,这给数据共享和模型开发带来了隐私挑战.
- 精确建模复杂的医疗时间序列数据对于推进医疗分析至关重要.
- 现有的生成模型在精确的分布建模和强大的患者隐私保护方面都在扎.
研究的目的:
- 引入CodeAR,这是一个新的生成模型,用于合成现实的私人电子健康记录 (EHR) 时间序列数据.
- 利用对离散令牌的自回归建模来提高医疗数据中的分布精度和因果推理.
- 通过防止训练数据记忆的VQ-VAE组件来确保患者的隐私.
主要方法:
- CodeAR使用矢量量化变量自编码器 (VQ-VAE) 来将连续的EHR数据转换为离散的令牌.
- 自动回归建模适用于这些离散的令牌,通过下一个令牌预测进行训练,以增强分布建模.
- 该模型捕捉因果关系并防止数据记忆的能力是其设计的核心.
主要成果:
- 与基线自回归和基于生成对抗网络 (GAN) 的模型相比,CodeAR表现优越.
- 使用最大平均差异 (MMD) 和对合成进行训练,对真实进行测试 (TSTR) 基准的评估证实了CodeAR的有效性.
- 该模型显示了对数据记忆的稳定性,确保了患者的隐私.
结论:
- 在离散令牌上的自回归建模对于医学时间序列合成非常有效.
- 代码AR提供了一个强大的解决方案,用于生成私有和准确的EHR数据,促进因果建模.
- 拟议的VQ-VAE方法有效地平衡了数据实用性与严格的患者隐私要求.
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