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

    16 Citations (Scopus)

    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
    Country/TerritoryHungary
    CityBudapest
    Period19/7/1419/7/19

    Bibliographical note

    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.

    Keywords

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

    ASJC Scopus subject areas

    • Software
    • Artificial Intelligence

    Fingerprint

    Dive into the research topics of 'Stochastic Imputation and Uncertainty-Aware Attention to EHR for Mortality Prediction'. Together they form a unique fingerprint.

    Cite this