期刊
APPLIED SCIENCES-BASEL
卷 9, 期 15, 页码 -出版社
MDPI
DOI: 10.3390/app9153041
关键词
multivariate time series classification; missing values; electronic health records; deep learning
类别
资金
- National Nature Science Foundation of China [61672241, U1611461]
- Cultivation Project of Major Basic Research of NSF-Guangdong Province [2016A030308013]
- Science and Technology Program of Guangzhou [201802010055]
Multivariate time series are often accompanied with missing values, especially in clinical time series, which usually contain more than 80% of missing data, and the missing rates between different variables vary widely. However, few studies address these missing rate differences and extract univariate missing patterns simultaneously before mixing them in the model training procedure. In this paper, we propose a novel recurrent neural network called variable sensitive GRU (VS-GRU), which utilizes the different missing rate of each variable as another input and learns the feature of different variables separately, reducing the harmful impact of variables with high missing rates. Experiments show that VS-GRU outperforms the state-of-the-art method in two real-world clinical datasets (MIMIC-III, PhysioNet).
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