DATA-GRU: Dual-Attention Time-Aware Gated Recurrent Unit for Irregular Multivariate Time Series

Qingxiong Tan, Mang Ye, Baoyao Yang , Siqi Liu , Andy Jinhua Ma, Terry Cheuk-Fung Yip, Grace Lai Hung Wong, Pong Chi Yuen *

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

62 Citations (Scopus)


Due to the discrepancy of diseases and symptoms, patients usually visit hospitals irregularly and different physiological variables are examined at each visit, producing large amounts of irregular multivariate time series (IMTS) data with missing values and varying intervals. Existing methods process IMTS into regular data so that standard machine learning models can be employed. However, time intervals are usually determined by the status of patients, while missing values are caused by changes in symptoms. Therefore, we propose a novel end-to-end Dual-Attention Time-Aware Gated Recurrent Unit (DATA-GRU) for IMTS to predict the mortality risk of patients. In particular, DATA-GRU is able to: 1) preserve the informative varying intervals by introducing a time-aware structure to directly adjust the influence of the previous status in coordination with the elapsed time, and 2) tackle missing values by proposing a novel dual-attention structure to jointly consider data-quality and medical-knowledge. A novel unreliability-aware attention mechanism is designed to handle the diversity in the reliability of different data, while a new symptom-aware attention mechanism is proposed to extract medical reasons from original clinical records. Extensive experimental results on two real-world datasets demonstrate that DATA-GRU can significantly outperform state-of-the-art methods and provide meaningful clinical interpretation.
Original languageEnglish
Pages (from-to)930-937
Number of pages8
JournalProceedings of the AAAI Conference on Artificial Intelligence
Issue number1
Publication statusPublished - 7 Feb 2020


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