Abstract
Electronic health records (EHRs) can be very difficult to analyze since they usually contain many missing values. To build an efficient predictive model, a complete dataset is necessary. An EHR usually contains high-dimensional longitudinal time series data. Most commonly used imputation methods do not consider the importance of temporal information embedded in EHR data. Besides, most time-dependent neural networks such as recurrent neural networks (RNNs) inherently consider the time steps to be equal, which in many cases, is not appropriate. This study presents a method using the gated recurrent unit (GRU), neural ordinary differential equations (ODEs), and Bayesian estimation to incorporate the temporal information and impute sporadically observed time series measurements in high-dimensional EHR data.
Highlights
Introduction and BackgroundSampled Time Series ImputationOne of the biggest challenges to work with electronic health record (EHR) data is that there are many missing values
We investigated time series imputation with irregular time gaps and propose a method based on neural ordinary differential equations (ODEs), recurrent neural networks (RNNs), and Bayesian estimation
Our research focuses on a time series imputation method that can deal with sporadically observed time series measurements obtained
Summary
One of the biggest challenges to work with electronic health record (EHR) data is that there are many missing values. This issue incorporates uncertainty in the predictive model if the missing instances are imputed. We investigated time series imputation with irregular time gaps and propose a method based on neural ordinary differential equations (ODEs), recurrent neural networks (RNNs), and Bayesian estimation. This method offers a robust imputation of sporadically sampled multivariate time series measurements obtained from different patients
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