Uncertainty-Aware Variational-Recurrent Imputation Network for Clinical Time Series

Ahmad Wisnu Mulyadi, Eunji Jun, Heung Il Suk

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


Electronic health records (EHR) consist of longitudinal clinical observations portrayed with sparsity, irregularity, and high dimensionality, which become major obstacles in drawing reliable downstream clinical outcomes. Although there exist great numbers of imputation methods to tackle these issues, most of them ignore correlated features, temporal dynamics, and entirely set aside the uncertainty. Since the missing value estimates involve the risk of being inaccurate, it is appropriate for the method to handle the less certain information differently than the reliable data. In that regard, we can use the uncertainties in estimating the missing values as the fidelity score to be further utilized to alleviate the risk of biased missing value estimates. In this work, we propose a novel variational-recurrent imputation network, which unifies an imputation and a prediction network by taking into account the correlated features, temporal dynamics, as well as uncertainty. Specifically, we leverage the deep generative model in the imputation, which is based on the distribution among variables, and a recurrent imputation network to exploit the temporal relations, in conjunction with utilization of the uncertainty. We validated the effectiveness of our proposed model on two publicly available real-world EHR datasets: 1) PhysioNet Challenge 2012 and 2) MIMIC-III, and compared the results with other competing state-of-the-art methods in the literature.

Original languageEnglish
JournalIEEE Transactions on Cybernetics
Publication statusPublished - 2022 Sept 1

Bibliographical note

Funding Information:
This work was supported in part by the Institute of Information and communications Technology Planning and Evaluation (IITP) grant funded by the Korea Government (Department of Artificial Intelligence, Korea University, MSIT) under Grant 2019-0-00079 and in part by the National Research Foundation of Korea (NRF) Grant Funded by the Korea Government (MSIT) under Grant 2019R1A2C1006543.

Publisher Copyright:


  • Bioinformatics
  • deep generative model
  • deep learning
  • electronic health records (EHR)
  • in-hospital mortality prediction
  • missing value imputation
  • time-series modeling
  • uncertainty

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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