时间序列健康数据中缺失值的深度归算:与基准测试的审查
Maksims Kazijevs1, Manar D Samad1
1Department of Computer Science, Tennessee State University, Nashville, TN 37209, United States.
Journal of biomedical informatics
|July 10, 2023
概括
对多变量时间序列 (MTS) 数据归算的深度学习方法的基准测试没有显示出单一的最佳方法. 性能因数据特性而异,但深度学习为医疗信息学提供了卓越的数据质量.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 医疗信息学 医疗信息学
背景情况:
- 在多变量时间序列 (MTS) 数据中准确的缺失值赋值对于可靠的预测建模至关重要.
- 虽然深度学习方法有前途,但它们的评估范围有限.
- 现有的研究往往缺乏在各种数据集和缺失数据场景中进行全面的基准测试.
研究的目的:
- 对MTS数据进行最先进的深度归算方法的以数据为中心的基准测试.
- 评估不同缺失特征的各种健康数据集的归算性能.
- 将深度学习归算与传统方法进行比较.
主要方法:
- 进行了六个以数据为中心的实验,以对深度归算方法进行基准测试.
- 五个不同的时间序列健康数据集被用于评估.
- 基于数据类型,变量统计,缺失值率和缺失类型来评估绩效.
主要成果:
- 没有任何一个深度归算方法在所有测试的数据集中显示出优异的性能.
- 计算效率受到数据特征和缺失数据模式的重大影响.
- 结合横截面和纵向归算的深度学习方法比传统方法提高了数据质量.
结论:
- 对MTS数据的归算方法的选择必须以数据为中心,以优化预测模型的性能.
- 深度学习归算方法在医疗信息学中是实用的和有益的,尽管计算成本很高.
- 需要进一步的研究,以调整深度归算策略以应对特定的数据挑战.
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