XU-NetI: Simple U-Shaped Encoder-Decoder Network for Accurate Imputation of Multivariate Missing Data

Abstract

Intensive care unit (ICU) patients often have multiple vital signs monitored continuously. However, missing data is common in ICU settings, negatively impacting clinical decision-making and patient outcomes. In this study, we propose a multivariate data imputation method based on simple U-Shaped encoder-decoder network imputation (XU-NetI) method to learn the underlying patterns in the data and generate imputations for missing values of vital signs data with ICU patients. To evaluate the performance of our imputation methods, we employed a publicly available database such the medical information mart for intensive care III (MIMIC III) v1.4. Our proposed model has been developed to analyze 219.281 vital sign worth of data, focusing on eight essential vital sign features: body temperature, heart rate, respiration rate, systolic blood pressure, diastolic blood pressure, mean blood pressure, oxygen saturation, and glucose. The evaluation results demonstrates the effectiveness of the imputation techniques in improving the accuracy of predictive models. We compared our approach to other state-of-the-art imputation methods including Autoencoder and Convolutional Neural Networks. As a result found, our technique with XU-NetI architecture outperformed them, in terms of root mean square error (RSME) by approximately 0.01, mean absolute error (MAE) by approximately 0.009, and R square (R2) by approximately 0.99. Our method has the potential to enhance clinical decision-making and improve patient outcomes.