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通过时间生成对抗网络对电子医疗记录进行归算

Yunfei Yin¹, Zheng Yuan², Islam Md Tanvir²

¹College of Computer Science, Chongqing University, Chongqing, 400044, China. yinyunfei@cqu.edu.cn.

|June 26, 2024

概括

这项研究介绍了UGAN-GRUD,这是一个改进的方法来填补缺少的电子医疗记录数据. UGAN-GRUD显著提高了归算准确性,特别是在缺失率高的数据集中.

科学领域:

生物医学信息学生物医学信息学
机器学习机器学习
数据科学数据科学数据科学

背景情况:

电子医疗记录的丢失阻碍了生物医学数据分析.
目前的生成对抗网络 (GAN) 方法在高缺失数据率下扎.

研究的目的:

提高电子医疗记录的归算准确度,因为电子医疗记录的丢失率很高.
开发一种新的方法来解决数据归算中的GAN限制.

主要方法:

拟议的UGAN-GRUD,结合了不确定性生成对抗网络 (UGAN) 和门反复单位衰减 (GRUD).
UGAN学习数据分布和不确定性;GRUD使用时间衰减来补偿时间关系.
设计并集成UGAN和GRUD网络.

主要成果:

在公共生物医学数据集上,UGAN-GRUD在最先进的方法上表现出优越的性能.
在根平均平方误差 (RMSE) 和平均绝对百分比误差 (MAPE) 中获得了13%的平均改善.

结论:

乌干格鲁德有效地解决了指责丢失的电子医疗记录数据的挑战.
拟议的方法为生物医学数据完整性和应用提供了显著的进步.

关键词:

协会关系协会关系电子医疗记录电子医疗记录生成性的对抗性网络.缺失的价值是错失的值.时间衰变时间衰变.

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