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arXiv · 1703.07004

The Use of Autoencoders for Discovering Patient Phenotypes

Harini Suresh·Peter Szolovits·Marzyeh Ghassemi
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Abstract

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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Harini Suresh, Peter Szolovits, Marzyeh Ghassemi. 2017-03-20. The Use of Autoencoders for Discovering Patient Phenotypes. https://arxiv.org/abs/1703.07004

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