Integrative Analysis of Multiple Data Streams in Critical Care Medicine via Deep Gaussian Processes with Stochastic Imputation
Healthcare data, particularly in critical care settings, presents three key challenges for analysis. First, physiological measurements come from different sources but are inherently related. Yet, traditional methods often treat each measurement type independently, losing valuable information about their relationships. Second, medical measurements are collected at irregular intervals, and these sampling times can carry clinical meaning. Finally, whilst several imputation methods exist to tackle the common problem of missing values, they often fail to address the temporal nature of the data or provide estimates of uncertainty in their predictions. We propose using deep Gaussian process emulation using stochastic imputation, a methodology initially conceived to deal with computationally expensive calculation for uncertainty quantification, to solve the problem of handling missing values that naturally occur in critical care data. This method leverages longitudinal and cross-sectional information and provides uncertainty estimation for the imputed values. Our evaluation of a clinical dataset shows that the proposed method performs better than conventional methods, such as multiple imputations with chained equations (MICE), last-known value imputation, and individually fitted Gaussian Processes (GPs).