TY - JOUR
T1 - Uncertainty-Aware Variational-Recurrent Imputation Network for Clinical Time Series
AU - Mulyadi, Ahmad Wisnu
AU - Jun, Eunji
AU - Suk, Heung Il
N1 - 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:
© 2013 IEEE.
PY - 2022/9/1
Y1 - 2022/9/1
N2 - 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.
AB - 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.
KW - Bioinformatics
KW - deep generative model
KW - deep learning
KW - electronic health records (EHR)
KW - in-hospital mortality prediction
KW - missing value imputation
KW - time-series modeling
KW - uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85102286823&partnerID=8YFLogxK
U2 - 10.1109/TCYB.2021.3053599
DO - 10.1109/TCYB.2021.3053599
M3 - Article
C2 - 33661746
AN - SCOPUS:85102286823
SN - 2168-2267
VL - 52
SP - 9684
EP - 9694
JO - IEEE Transactions on Cybernetics
JF - IEEE Transactions on Cybernetics
IS - 9
ER -