Missing data is a common problem in the intensive care unit as a variety of factors contribute to incomplete data collection in this clinical setting. This missing data has a significant impact on the accuracy and validity of statistical analyses and prognostic models. Several imputation methods can be used to estimate the missing values based on the available data. Although simple imputations with mean or median generate reasonable results in terms of mean absolute error, they do not account for the currentness of the data. Furthermore, heterogeneous time span of data records adds to this complexity, especially in high-frequency intensive care unit datasets. Therefore, we present DeepTSE, a deep model that is able to cope with both, missing data and heterogeneous time spans. We achieved promising results on the MIMIC-IV dataset that can compete with and even outperform established imputation methods.
Keywords: Deep Embedding; ICU; MIMIC IV; Machine Learning; Patient Modeling; Time-Sensitive Data Imputation.