XU-NetI: Simple U-shaped encoder-decoder network for accurate imputation of multivariate missing data

Abstract

Patients in intensive care unit (ICU) 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 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 this study's imputation methods, this study employed a publicly available database such the medical information mart for intensive care III (MIMIC III) v1.4. In this study 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. This study compared the XU-NetI approach to other state-of-the-art imputation methods including Autoencoder and Convolutional Neural Networks. As a result found, the technique using 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. The XU-NetI method has the potential to enhance clinical decision-making and improve patient outcomes.

Keywords

• Missing data imputation
• U-shaped encoder-decoder network
• Convolutional neural network
• Deep Learning