  Despite the eminent successes of deep neural networks, many architectures are
often hard to transfer to irregularly-sampled and asynchronous time series that
commonly occur in real-world datasets, especially in healthcare applications.
This paper proposes a novel approach for classifying irregularly-sampled time
series with unaligned measurements, focusing on high scalability and data
efficiency. Our method SeFT (Set Functions for Time Series) is based on recent
advances in differentiable set function learning, extremely parallelizable with
a beneficial memory footprint, thus scaling well to large datasets of long time
series and online monitoring scenarios. Furthermore, our approach permits
quantifying per-observation contributions to the classification outcome. We
extensively compare our method with existing algorithms on multiple healthcare
time series datasets and demonstrate that it performs competitively whilst
significantly reducing runtime.
