# The Use of Autoencoders for Discovering Patient Phenotypes

arXiv: https://arxiv.org/abs/1703.07004 · HTML: https://mlsanity.com/paper/1703.07004/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2017.

## Built on by (most cited first, 3 of 3)

- [Supervised multi-specialist topic model with applications on large-scale electronic health record data](https://mlsanity.com/paper/2105.01238/) (2021)
- [PheME: A deep ensemble framework for improving phenotype prediction from multi-modal data](https://mlsanity.com/paper/2303.10794/) (2023)
- [Predicting Intraoperative Hypoxemia with Hybrid Inference Sequence Autoencoder Networks](https://mlsanity.com/paper/2104.14756/) (2021)

## Abstract (arXiv)

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.