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 hypoglycaemia 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.