Stochastic Imputation and Uncertainty-Aware Attention to EHR for Mortality Prediction

Eunji Jun, Ahmad Wisnu Mulyadi, Heung Il Suk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2019 International Joint Conference on Neural Networks, IJCNN 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728119854
DOIs
Publication statusPublished - 2019 Jul
Event2019 International Joint Conference on Neural Networks, IJCNN 2019 - Budapest, Hungary
Duration: 2019 Jul 142019 Jul 19

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2019-July

Conference

Conference2019 International Joint Conference on Neural Networks, IJCNN 2019
CountryHungary
CityBudapest
Period19/7/1419/7/19

Fingerprint

Health
Time series
Uncertainty

Keywords

  • Bayesian framework
  • Deep learning
  • Electronic health records
  • Missing data imputation

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Jun, E., Mulyadi, A. W., & Suk, H. I. (2019). Stochastic Imputation and Uncertainty-Aware Attention to EHR for Mortality Prediction. In 2019 International Joint Conference on Neural Networks, IJCNN 2019 [8852132] (Proceedings of the International Joint Conference on Neural Networks; Vol. 2019-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2019.8852132

Stochastic Imputation and Uncertainty-Aware Attention to EHR for Mortality Prediction. / Jun, Eunji; Mulyadi, Ahmad Wisnu; Suk, Heung Il.

2019 International Joint Conference on Neural Networks, IJCNN 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8852132 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2019-July).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Jun, E, Mulyadi, AW & Suk, HI 2019, Stochastic Imputation and Uncertainty-Aware Attention to EHR for Mortality Prediction. in 2019 International Joint Conference on Neural Networks, IJCNN 2019., 8852132, Proceedings of the International Joint Conference on Neural Networks, vol. 2019-July, Institute of Electrical and Electronics Engineers Inc., 2019 International Joint Conference on Neural Networks, IJCNN 2019, Budapest, Hungary, 19/7/14. https://doi.org/10.1109/IJCNN.2019.8852132
Jun E, Mulyadi AW, Suk HI. Stochastic Imputation and Uncertainty-Aware Attention to EHR for Mortality Prediction. In 2019 International Joint Conference on Neural Networks, IJCNN 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8852132. (Proceedings of the International Joint Conference on Neural Networks). https://doi.org/10.1109/IJCNN.2019.8852132
Jun, Eunji ; Mulyadi, Ahmad Wisnu ; Suk, Heung Il. / Stochastic Imputation and Uncertainty-Aware Attention to EHR for Mortality Prediction. 2019 International Joint Conference on Neural Networks, IJCNN 2019. Institute of Electrical and Electronics Engineers Inc., 2019. (Proceedings of the International Joint Conference on Neural Networks).
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