  We use autoencoders to create low-dimensional embeddings of underlying
patient phenotypes that we hypothesize are a governing factor in determining
how different patients will react to different interventions. We compare the
performance of autoencoders that take fixed length sequences of concatenated
timesteps as input with a recurrent sequence-to-sequence autoencoder. We
evaluate our methods on around 35,500 patients from the latest MIMIC III
dataset from Beth Israel Deaconess Hospital.
