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

  • Qingxiong Tan Hong Kong Baptist University
  • Mang Ye Hong Kong Baptist University
  • Baoyao Yang Hong Kong Baptist University
  • Siqi Liu Hong Kong Baptist University
  • Andy Jinhua Ma Sun Yat-Sen University
  • Terry Cheuk-Fung Yip The Chinese University of Hong Kong
  • Grace Lai-Hung Wong The Chinese University of Hong Kong
  • PongChi Yuen Hong Kong Baptist University

Abstract

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.

Published
2020-04-03
Section
AAAI Technical Track: Applications