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基于生成对抗网络的电子健康记录中不完整和不平衡数据的联合学习方法
Xutao Weng1, Hong Song1, Yucong Lin2
1School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China.
这项研究引入了一个统一的框架,即缺失值推算和不平衡学习生成对抗网络 (MVIIL-GAN),以同时处理不完整和不平衡的电子健康记录 (EHR) 数据,显著提高临床预测性能.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 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) 通常包含不完整和不平衡的数据,阻碍了准确的临床预测.
- 之前处理这些问题单独的方法导致预测任务性能降低.
研究的目的:
- 提出一个统一的框架,同时处理EHR中不完整和不平衡的数据.
- 开发和评估一个新的模型,MVIIL-GAN,用于数据归算和生成的联合学习.
主要方法:
- 开发了缺失值推算和不平衡学习生成对抗网络 (MVIIL-GAN).
- 员工联合学习用于高缺失率数据归算和有条件的EHR数据生成.
- 使用样本级和变量级的区分器来区分生成的数据.
主要成果:
- MVIIL-GAN将缺失值赋值和数据生成集成到一个单一的步骤中.
- 实现了改进的参数优化一致性和增强的预测任务性能.
- 在MIMIC-IV数据集上表现优于现有的方法,缺失和不平衡数据很高.
结论:
- 拟议的MVIIL-GAN框架有效地应对不完整和不平衡的EHR数据的挑战.
- 在MVIIL-GAN中进行联合学习可以提高临床预测的准确性.
- MVIIL-GAN为在临床研究中利用EHR数据提供了一个有前途的方法.
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