Medical time-series datasets have unique characteristics that make prediction tasks challenging. Most notably, patient trajectories often contain longitudinal variations in their input-output relationships, generally referred to as temporal conditional shift. Designing sequence models capable of adapting to such time-varying distributions remains a prevailing problem. To address this we present Model-Attentive Ensemble learning for Sequence modeling (MAES). MAES is a mixture of time-series experts which leverages an attention-based gating mechanism to specialize the experts on different sequence dynamics and adaptively weight their predictions. We demonstrate that MAES significantly out-performs popular sequence models on datasets subject to temporal shift.
@article{arxiv.2102.11500,
title = {Model-Attentive Ensemble Learning for Sequence Modeling},
author = {Victor D. Bourgin and Ioana Bica and Mihaela van der Schaar},
journal= {arXiv preprint arXiv:2102.11500},
year = {2021}
}