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Uncertainty-Gated Stochastic Sequential Model for EHR Mortality Prediction.

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    This study introduces a new recurrent network for electronic health records (EHRs) that handles missing data by estimating uncertainty. The model improves in-hospital mortality prediction by accounting for imputation confidence.

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    Area of Science:

    • Health Informatics
    • Machine Learning
    • Data Science

    Background:

    • Electronic health records (EHRs) present significant data challenges due to their non-stationary, heterogeneous, noisy, and sparse nature.
    • Data sparseness, primarily from missing values, hinders pattern discovery and accurate predictive modeling in EHRs.
    • Existing imputation methods often overlook the fidelity or confidence of imputed values, potentially degrading downstream task performance.

    Purpose of the Study:

    • To develop a novel variational recurrent network for EHR data that addresses data sparseness and uncertainty.
    • To improve the accuracy of in-hospital mortality prediction by incorporating imputation uncertainty into the model.
    • To enable end-to-end learning of imputation and prediction tasks within a single model stream.

    Main Methods:

    • A variational recurrent network is proposed to estimate the distribution (mean and variance) of missing variables, quantifying imputation uncertainty.
    • The model updates hidden states by incorporating the variance of imputed values, enabling uncertainty propagation over time.
    • The network jointly learns imputation and in-hospital mortality prediction parameters in an end-to-end manner.

    Main Results:

    • The proposed method effectively estimates imputation uncertainty, showing a strong correlation between uncertainty estimates and mean absolute error (MAE).
    • The model achieved superior performance in predicting in-hospital mortality compared to state-of-the-art methods on MIMIC-III and PhysioNet challenge 2012 datasets.
    • The variational recurrent network successfully integrated imputation fidelity into the mortality prediction task.

    Conclusions:

    • The novel variational recurrent network offers a robust approach to handling missing data in EHRs by modeling imputation uncertainty.
    • This method enhances the reliability and accuracy of predictive tasks, such as in-hospital mortality prediction, by considering data imputation quality.
    • The findings demonstrate the potential of uncertainty-aware deep learning models for improving clinical decision support systems using EHR data.