TY - GEN
T1 - Stochastic Imputation and Uncertainty-Aware Attention to EHR for Mortality Prediction
AU - Jun, Eunji
AU - Mulyadi, Ahmad Wisnu
AU - Suk, Heung Il
N1 - Funding Information:
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.2017-0-00053, A Technology Development of Artificial Intelligence Doctors for Cardiovascular Disease).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Electronic health records (EHR) have become an important source of a patient data but characterized by a variety of missing values. Using the variational inference of Bayesian framework, variational autoencoder (VAE), a deep generative model, has been shown to be efficient and accurate to capture the latent structure of complex high-dimensional data. Recently, it has been used for missing data imputation. In this paper, we propose a general framework that incorporates effective missing data imputation using VAE and multivariate time series prediction. We utilize the uncertainty obtained from the generative network of the VAE and employ uncertainty-aware attention in imputing the missing values. We evaluated the performance of our architecture on real-world clinical dataset (MIMIC-III) for in-hospital mortality prediction task. Our results showed higher performance than other competing methods in mortality prediction task.
AB - Electronic health records (EHR) have become an important source of a patient data but characterized by a variety of missing values. Using the variational inference of Bayesian framework, variational autoencoder (VAE), a deep generative model, has been shown to be efficient and accurate to capture the latent structure of complex high-dimensional data. Recently, it has been used for missing data imputation. In this paper, we propose a general framework that incorporates effective missing data imputation using VAE and multivariate time series prediction. We utilize the uncertainty obtained from the generative network of the VAE and employ uncertainty-aware attention in imputing the missing values. We evaluated the performance of our architecture on real-world clinical dataset (MIMIC-III) for in-hospital mortality prediction task. Our results showed higher performance than other competing methods in mortality prediction task.
KW - Bayesian framework
KW - Deep learning
KW - Electronic health records
KW - Missing data imputation
UR - http://www.scopus.com/inward/record.url?scp=85073253420&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2019.8852132
DO - 10.1109/IJCNN.2019.8852132
M3 - Conference contribution
AN - SCOPUS:85073253420
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2019 International Joint Conference on Neural Networks, IJCNN 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Joint Conference on Neural Networks, IJCNN 2019
Y2 - 14 July 2019 through 19 July 2019
ER -