  Time series data constitutes a distinct and growing problem in machine
learning. As the corpus of time series data grows larger, deep models that
simultaneously learn features and classify with these features can be
intractable or suboptimal. In this paper, we present feature learning via long
short term memory (LSTM) networks and prediction via gradient boosting trees
(XGB). Focusing on the consequential setting of electronic health record data,
we predict the occurrence of hypoxemia five minutes into the future based on
past features. We make two observations: 1) long short term memory networks are
effective at capturing long term dependencies based on a single feature and 2)
gradient boosting trees are capable of tractably combining a large number of
features including static features like height and weight. With these
observations in mind, we generate features by performing "supervised"
representation learning with LSTM networks. Augmenting the original XGB model
with these features gives significantly better performance than either
individual method.
