SQUIREDL: Sparse Sequence-to-Sequence Uncertainty Estimation in Evidential Deep Learning

Machine Learning models typically assume that time series are regularly spaced, however this is often unrealistic in healthcare, where missing data recordings are common. In this context, uncertainty estimates play a pivotal role, as they can enable confident and non-confident predictions to be distinguished. We propose SQUIREDL, a novel uncertainty-aware sequence-to-sequence prediction method for sparse healthcare time series. Specifically, we enhance the state-of-the-art evidential regression framework, widely used for uncertainty estimation, to handle missing data. Following data imputation with an Akima spline-based method, we modify the loss function of evidential regression by assigning different weights to imputed and observed data points, to offer more reliable uncertainty estimates. Additionally, we examine a variety of metrics for assessing the success of uncertainty estimations on sequence-to-sequence predictions, providing a reliable way to evaluate the models in a medical setting. Our proposal is demonstrated in two clinical applications. In continuous glucose monitoring, we use sequence-to-sequence prediction to obtain the hypoglycemia risk from glucose sensor readings. Our approach captures the ground truth risk values 30% more accurately, bringing consistent improvements in both uncertainty-aware and accuracy-based metrics. Similarly, in COVID-19 hospital admissions data, we achieve a 22% improvement in the accuracy of uncertainty-aware predictions, enabling better resource planning.