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Time series data constitutes a distinct and growing problem in machinelearning. As the corpus of time series data grows larger, deep models thatsimultaneously learn features and classify with these features can beintractable or suboptimal. In this paper, we present feature learning via longshort 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 onpast features. We make two observations: 1) long short term memory networks areeffective at capturing long term dependencies based on a single feature and 2)gradient boosting trees are capable of tractably combining a large number offeatures including static features like height and weight. With theseobservations in mind, we generate features by performing "supervised"representation learning with LSTM networks. Augmenting the original XGB modelwith these features gives significantly better performance than eitherindividual method.

Hybrid Gradient Boosting Trees and Neural Networks for Forecasting Operating Room Data