  The increase in availability of longitudinal electronic health record (EHR)
data is leading to improved understanding of diseases and discovery of novel
phenotypes. The majority of clustering algorithms focus only on patient
trajectories, yet patients with similar trajectories may have different
outcomes. Finding subgroups of patients with different trajectories and
outcomes can guide future drug development and improve recruitment to clinical
trials. We develop a recurrent neural network autoencoder to cluster EHR data
using reconstruction, outcome, and clustering losses which can be weighted to
find different types of patient clusters. We show our model is able to discover
known clusters from both data biases and outcome differences, outperforming
baseline models. We demonstrate the model performance on $29,229$ diabetes
patients, showing it finds clusters of patients with both different
trajectories and different outcomes which can be utilized to aid clinical
decision making.
