Comparison of Gaussian Processes Methods to Linear methods for Imputation of Sparse Physiological Time Series

Physiological timeseries such as vital signs contain important information about a patient and are used in different clinical application; however, they suffer from missing values and sampling irregularity. In recent years, Gaussian Processes have been used as sophisticated nonlinear value imputation methods on time series, however there is a lack of comparison to other simpler methods. This paper compares the ability of five methods that can be used in missing data imputation in physiological time series. These models are linear interpolation as the baseline, cubic spline interpolation, and three non-linear methods: Single Task Gaussian Processes, Multi-Task Gaussian Processes, and Multivariate Imputation Chained Equations. We used seven intraoperative physiological time series from 27,481 patients. Piecewise aggregate approximation was employed as a dimensionality reduction and resampling strategy. Linear interpolation and cubic splining show overall superiority in prediction of the missing values, compared to the other complex models. The performance of the kernel-based methods suggest that they are highly sensitive to the kernel width and require incorporation of domain knowledge for fine-tuning.