合成数据生成的生成模型:对药理动力学/药理动力学数据的应用.
Yulun Jiang1, Alberto García-Durán2, Idris Bachali Losada2
1School of Computer and Communication Science, Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland.
Journal of pharmacokinetics and pharmacodynamics
|August 27, 2024
概括
使用像MLP cGAN这样的深度学习模型生成合成患者数据可以改善临床研究的数据访问和可用性,特别是对于代表性不足的患者群体.
科学领域:
- 临床药理学 临床药理学
- 生物统计学 生物统计学
- 机器学习 机器学习
背景情况:
- 合成患者数据生成对于实现数据访问和增强数据集至关重要,特别是在代表性不足的人群中.
- 深度学习的生成方法为创建现实的合成数据提供了先进的解决方案.
研究的目的:
- 为了对最先进的深度学习生成方法进行基准测试,用于合成患者数据生成.
- 在各种临床数据集和场景中评估模型性能.
主要方法:
- 实施并比较了多层感知子调节生成对抗神经网络 (MLP cGAN),时间序列生成对抗网络 (TimeGAN) 和概率自行回归 (PAR) 模型.
- 使用歧视性和预测性得分,统计测试 (科尔莫戈罗夫-斯米尔诺夫,奇方),以及与药理学相关的指标来评估绩效.
主要成果:
- 在大多数评估指标中,MLP cGAN显示出最佳的整体表现.
- 该研究证实了合成数据对增强和共享专有临床数据的有用性.
结论:
- 深度学习生成模型,特别是MLP cGAN,对于生成高质量的合成患者数据是有效的.
- 合成数据生成具有促进临床药理学研究和数据可访问性的巨大潜力.
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