文献类型：期刊文献

英文题名：An Integrated Resampling Methods for Imbalanced Sporadic Temporal Data in EHRs

作者：Ye, Qi[1]; Kuroda, Tomohiro[2]; Ruan, Tong[3]; Zhang, Wenlong[3]; Ge, Xiaoling[4]

机构：[1] East China University of Science and Technology, School of Information Science and Technology, Shanghai, China; [2] Kyoto University Hospital, Kyoto, Japan; [3] East China University of Sci.Tech., School of Info. Sci.Tech., Shanghai, China; [4] Children's Hospital of Fudan University, Department of Information, Shanghai, China

年份：2021

起止页码：3129

外文期刊名：Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

收录：EI(收录号：20220911712530)

语种：英文

摘要：Most real-world applications in EHRs involve temporal data with skewed distributions. The imbalanced classification problem becomes more difficult in sporadic temporal data that variables exist on correlation and have some missing values. A common solution to classification tasks with imbalanced data is the oversampling methods, which generate new samples to re-balancing the classes. However, traditional oversampling methods usually change the distribution, thereby leading to bias. This paper proposed a self-adaptive integrated oversampling method for imbalanced sporadic temporal data in EHRs. The masking vectors and density vectors have been introduced to measure missing value distribution of samples, and the minority samples are divided into high density samples and sparse density samples. We extend the resampling strategies combining a subsample alignment method and structure preserving oversampling method. The weight of sample difference is used to improve classification performance. Furthermore, the filter mechanism is proposed to remove the noise samples with good efficiency. The experimental results show that the proposed method increases performance compared to traditional resampling methods in terms of AUC, F1, and G-mean evaluation metrics. ? 2021 IEEE.