The Use of Autoencoders for Discovering Patient Phenotypes

20 Mar 2017  ·  Harini Suresh, Peter Szolovits, Marzyeh Ghassemi ·

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.

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