特定于亚种群的合成电子健康记录可以增加死亡率预测性能
Oriel Perets1, Nadav Rappoport1
1Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, David Ben Gurion Blvd 1, Be'er Sheva, 8499000, Israel.
JAMIA open
|August 13, 2025
概括
本研究引入了一个使用生成对抗网络 (GAN) 来创建合成数据的框架,以提高电子健康记录 (EHR) 中代表性不足的子群体的预测模型性能. 这增强了公平的医疗保健分析.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 数据科学数据科学数据科学
背景情况:
- 电子健康记录 (EHR) 中的偏见代表性导致预测模型对代表性不足的子群体的表现不佳.
- 这种差异阻碍了人工智能在医疗保健中的公平应用.
研究的目的:
- 提出和评估一个框架,以提高EHR的公平预测性能.
- 为了解决各个子群体的绩效差异.
主要方法:
- 开发了一个使用生成对抗网络 (GAN) 来生成特定亚群的合成数据的框架.
- 增强原始训练数据集与合成数据.
- 采用集体方法,为每个子群体培养不同的预测模型.
主要成果:
- 根据MIMIC衍生数据集进行评估,该框架改善了接受器运行特征曲线下面的区域 (ROCAUC) 代表人数不足的子群体的8%至31%.
- 在预测模型中显著缓解了性能差异.
结论:
- 基于GAN的新框架和整体预测方法有效地提高了各亚群体的预测公平性.
- 公共可用的代码和模型支持进一步的研究和在医疗保健中采用公平的预测分析.
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