  In this work, we investigate unsupervised representation learning on medical
time series, which bears the promise of leveraging copious amounts of existing
unlabeled data in order to eventually assist clinical decision making. By
evaluating on the prediction of clinically relevant outcomes, we show that in a
practical setting, unsupervised representation learning can offer clear
performance benefits over end-to-end supervised architectures. We experiment
with using sequence-to-sequence (Seq2Seq) models in two different ways, as an
autoencoder and as a forecaster, and show that the best performance is achieved
by a forecasting Seq2Seq model with an integrated attention mechanism, proposed
here for the first time in the setting of unsupervised learning for medical
time series.
