Mortality Prediction with Bidirectional Coupled and Gumbel Subset Network on Irregularly Multivariate Time Series

Qinfen Wang, Siyuan Ren, Yong Xia

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Mortality prediction is a crucial challenge because of multivariate time series (MTS) complexity, which are sparse, irregularly, asynchronous and hold missing values for various reasons in a single acquisition. Various methods are proposed to deal with missing values for the final mortality prediction. However, existing models only capture the temporal dependencies within a time series and are inefficient to capture the dependencies between time series to rebuild missing values for mortality prediction. To address these challenges, in this paper, we present an end-to-end imputation and mortality prediction model, named bidirectional coupled and Gumbel subset network (BiCGSN), for mortality prediction with such irregularly multivariate time series. Our proposed model (BiCGSN) uses a recurrent network to learn the temporal dependencies (intra-time series couplings) and uses a Gumbel selector on multi-head attention to obtain the relationship between the variables (inter-time series couplings) in the forward and backward directions. Then the learned bidirectional inter-and intra-time series couplings are fused to impute missing values for further mortality prediction. We evaluate our model on PhysioNet2012 and COVID-19 datasets to imputation and predict mortality. Experiments show that BiCGSN obtains the AUC 0.869 and 0.911 on two real-world datasets respectively and outperforms all the baselines.

Original languageEnglish
Title of host publicationICSP 2022 - 2022 16th IEEE International Conference on Signal Processing, Proceedings
EditorsBaozong Yuan, Qiuqi Ruan, Shikui Wei, Gaoyun An
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages468-473
Number of pages6
ISBN (Electronic)9781665460569
DOIs
StatePublished - 2022
Event16th IEEE International Conference on Signal Processing, ICSP 2022 - Beijing, China
Duration: 21 Oct 202224 Oct 2022

Publication series

NameInternational Conference on Signal Processing Proceedings, ICSP
Volume2022-October

Conference

Conference16th IEEE International Conference on Signal Processing, ICSP 2022
Country/TerritoryChina
CityBeijing
Period21/10/2224/10/22

Keywords

  • couplings
  • gumbel
  • missing data
  • multivariate time series
  • self-attention

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