一种基于临床条件生成对抗网络的新型缺失数据归算方法,应用于EHR数据集
Michele Bernardini1, Anastasiia Doinychko2, Luca Romeo3
1Department of Information Engineering (DII), Università Politecnica delle Marche, Ancona, Italy.
Computers in biology and medicine
|July 2, 2023
概括
这项研究引入了一种新的临床条件生成对抗网络 (ccGAN),用于归因电子健康记录 (EHR) 中缺少的数据. ccGAN方法显著改善了数据归算和预测性能,超过了现有策略.
科学领域:
- 机器学习 机器学习
- 生物医学信息学 生物医学信息学
- 数据科学数据科学数据科学
背景情况:
- 缺失的数据是电子健康记录 (EHR) 中的一个重大挑战,导致时空空间稀疏.
- 现有的数据归算方法往往缺乏模型集成,并没有针对EHR的具体情况进行优化,并利用有限的特征信息.
研究的目的:
- 提出使用临床条件生成对抗网络 (ccGAN) 的EHR数据的新型数据归算策略.
- 通过利用非线性,多变量信息和处理EHR中高失踪率来解决当前方法的局限性.
主要方法:
- 开发了一个临床条件生成对抗网络 (ccGAN) 用于数据归算.
- 在可观测和完全注释的数据上依据归算策略来管理高缺失率.
- 在真实多糖尿病中心的EHR数据集和基准EHR数据集上评估性能.
主要成果:
- 与最先进的方法相比,ccGAN在归算性能上获得了19.79%的收益,在预测性能上获得了1.60%的收益.
- 在各种失踪率中表现出强性,在失踪率高的条件下,高达1.61%的性能提升.
- 与现有方法相比,统计学意义得到证实.
结论:
- 拟议的ccGAN为EHR数据提供了一种优越的数据归算策略,优于目前的方法.
- 这种方法有效地处理了EHR中缺少数据的固有挑战,提高了数据质量和预测准确性.
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